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Energies, Volume 9, Issue 4 (April 2016) – 85 articles

Cover Story (view full-size image): Cavitation in hydro turbines occurs when the pressure falls sufficiently low in some regions of the flow so that water vapor bubbles are formed. This is a complex phenomenon and can cause damaging effects such as vibration, blade surface erosion and performance loss. Despite the fact that crossflow turbines have been used in small-scale hydropower systems for a long time, cavitation has not been studied in these turbines. In this study, the first of its kind for crossflow turbines, we show that cavitation occurs in low-head crossflow hydro turbines. Cavitation inception was characterized using three-dimensional Reynolds-averaged Navier–Stokes computations with a homogeneous, free-surface two-phase flow model. It is demonstrated that cavitation occurs in the second stage of the turbine and was observed on the suction side near the inner edge of the blades. View the paper
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139 KiB  
Editorial
Hydrides: Fundamentals and Applications
by Craig M. Jensen, Etsuo Akiba and Hai-Wen Li
Energies 2016, 9(4), 308; https://doi.org/10.3390/en9040308 - 22 Apr 2016
Viewed by 4360
Abstract
Both the Japanese and Hawaiian archipelagos are both completely devoid of petroleum resources.[...] Full article
(This article belongs to the Special Issue Hydrides: Fundamentals and Applications)
8931 KiB  
Article
An Optimal Integrated Control Scheme for Permanent Magnet Synchronous Generator-Based Wind Turbines under Asymmetrical Grid Fault Conditions
by Dan Wang, Chongru Liu and Gengyin Li
Energies 2016, 9(4), 307; https://doi.org/10.3390/en9040307 - 22 Apr 2016
Cited by 9 | Viewed by 5792
Abstract
In recent years, the increasing penetration level of wind energy into power systems has brought new issues and challenges. One of the main concerns is the issue of dynamic response capability during outer disturbance conditions, especially the fault-tolerance capability during asymmetrical faults. In [...] Read more.
In recent years, the increasing penetration level of wind energy into power systems has brought new issues and challenges. One of the main concerns is the issue of dynamic response capability during outer disturbance conditions, especially the fault-tolerance capability during asymmetrical faults. In order to improve the fault-tolerance and dynamic response capability under asymmetrical grid fault conditions, an optimal integrated control scheme for the grid-side voltage-source converter (VSC) of direct-driven permanent magnet synchronous generator (PMSG)-based wind turbine systems is proposed in this paper. The optimal control strategy includes a main controller and an additional controller. In the main controller, a double-loop controller based on differential flatness-based theory is designed for grid-side VSC. Two parts are involved in the design process of the flatness-based controller: the reference trajectories generation of flatness output and the implementation of the controller. In the additional control aspect, an auxiliary second harmonic compensation control loop based on an improved calculation method for grid-side instantaneous transmission power is designed by the quasi proportional resonant (Quasi-PR) control principle, which is able to simultaneously restrain the second harmonic components in active power and reactive power injected into the grid without the respective calculation for current control references. Moreover, to reduce the DC-link overvoltage during grid faults, the mathematical model of DC-link voltage is analyzed and a feedforward modified control factor is added to the traditional DC voltage control loop in grid-side VSC. The effectiveness of the optimal control scheme is verified in PSCAD/EMTDC simulation software. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Structural diagram of a differential flatness-based (DFB) control system.</p>
Full article ">Figure 2
<p>Topological structure of a permanent magnet synchronous generator (PMSG) wind power system.</p>
Full article ">Figure 3
<p>DFB control system diagram of a PMSG grid-side voltage source converter (VSC).</p>
Full article ">Figure 4
<p>Diagram of the relationship between the stationary coordinate system and positive/negative synchronous rotating coordinate systems.</p>
Full article ">Figure 5
<p>Overall diagram of PMSG grid-side VSC improved control schemes.</p>
Full article ">Figure 6
<p>Simulation model of the PMSG wind power system in PSCAD.</p>
Full article ">Figure 7
<p>Simulation results of single-phase-to-ground grid fault condition. (<b>a1</b>–<b>a3</b>) DC voltage; (<b>b1</b>–<b>b3</b>) Second harmonic components of DC voltage; (<b>c1</b>–<b>c3</b>) Grid-side active power; (<b>d1</b>–<b>d3</b>) Second harmonic components of grid-side active power; (<b>e1</b>–<b>e3</b>) Grid-side reactive power; (<b>f1</b>–<b>f3</b>) Second harmonic components of grid-side reactive power; (<b>g1</b>–<b>g3</b>) Voltage at the low-voltage side of the transformer during grid short-circuit fault; (<b>h1</b>–<b>h3</b>) Current at the low-voltage side of the transformer during grid short-circuit fault.</p>
Full article ">Figure 7 Cont.
<p>Simulation results of single-phase-to-ground grid fault condition. (<b>a1</b>–<b>a3</b>) DC voltage; (<b>b1</b>–<b>b3</b>) Second harmonic components of DC voltage; (<b>c1</b>–<b>c3</b>) Grid-side active power; (<b>d1</b>–<b>d3</b>) Second harmonic components of grid-side active power; (<b>e1</b>–<b>e3</b>) Grid-side reactive power; (<b>f1</b>–<b>f3</b>) Second harmonic components of grid-side reactive power; (<b>g1</b>–<b>g3</b>) Voltage at the low-voltage side of the transformer during grid short-circuit fault; (<b>h1</b>–<b>h3</b>) Current at the low-voltage side of the transformer during grid short-circuit fault.</p>
Full article ">Figure 8
<p>Simulation results of single-phase-to-ground grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer + (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 8 Cont.
<p>Simulation results of single-phase-to-ground grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer + (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 8 Cont.
<p>Simulation results of single-phase-to-ground grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer + (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 8 Cont.
<p>Simulation results of single-phase-to-ground grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer + (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 9
<p>Simulation results of phase-to-phase grid fault condition. (<b>a1</b>–<b>a3</b>) DC voltage; (<b>b1</b>–<b>b3</b>) Second harmonic components of DC voltage; (<b>c1</b>–<b>c3</b>) Grid-side active power; (<b>d1</b>–<b>d3</b>) Second harmonic components of grid-side active power; (<b>e1</b>–<b>e3</b>) Grid-side reactive power; (<b>f1</b>–<b>f3</b>) Second harmonic components of grid-side reactive power; (<b>g1</b>–<b>g3</b>) Voltage at the low-voltage side of the transformer during grid short-circuit fault; (<b>h1</b>–<b>h3</b>) Current at the low-voltage side of the transformer during grid short-circuit fault.</p>
Full article ">Figure 9 Cont.
<p>Simulation results of phase-to-phase grid fault condition. (<b>a1</b>–<b>a3</b>) DC voltage; (<b>b1</b>–<b>b3</b>) Second harmonic components of DC voltage; (<b>c1</b>–<b>c3</b>) Grid-side active power; (<b>d1</b>–<b>d3</b>) Second harmonic components of grid-side active power; (<b>e1</b>–<b>e3</b>) Grid-side reactive power; (<b>f1</b>–<b>f3</b>) Second harmonic components of grid-side reactive power; (<b>g1</b>–<b>g3</b>) Voltage at the low-voltage side of the transformer during grid short-circuit fault; (<b>h1</b>–<b>h3</b>) Current at the low-voltage side of the transformer during grid short-circuit fault.</p>
Full article ">Figure 10
<p>Simulation results of phase-to-phase grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); <b>(b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 10 Cont.
<p>Simulation results of phase-to-phase grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); <b>(b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 10 Cont.
<p>Simulation results of phase-to-phase grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); <b>(b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
Full article ">Figure 10 Cont.
<p>Simulation results of phase-to-phase grid fault condition under different controllers. (<b>a1</b>) <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (conventional control); (<b>a3</b>) <span class="html-italic">P</span> (conventional control); (<b>a4</b>) Second harmonics of <span class="html-italic">P</span> (conventional control); (<b>a5</b>) <span class="html-italic">Q</span> (conventional control); (<b>a6</b>) Second harmonics of <span class="html-italic">Q</span> (conventional control); (<b>a7</b>) Voltages at the low-voltage side of transformer (conventional control); (<b>a8</b>) Currents at the low-voltage side of transformer (conventional control); (<b>b1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); (<b>b2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control I); <b>(b3</b>) <span class="html-italic">P</span> (DDSRF control I); (<b>b4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control I); (<b>b5</b>) <span class="html-italic">Q</span> (DDSRF control I); (<b>b6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control I); (<b>b7</b>) Voltages at the low-voltage side of transformer (DDSRF control I); (<b>b8</b>) Currents at the low-voltage side of transformer (DDSRF control I); (<b>c1</b>) <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (DDSRF control II); (<b>c3</b>) <span class="html-italic">P</span> (DDSRF control II); (<b>c4</b>) Second harmonics of <span class="html-italic">P</span> (DDSRF control II); (<b>c5</b>) <span class="html-italic">Q</span> (DDSRF control II); (<b>c6</b>) Second harmonics of <span class="html-italic">Q</span> (DDSRF control II); (<b>c7</b>) Voltages at the low-voltage side of transformer (DDSRF control II); (<b>c8</b>) Currents at the low-voltage side of transformer (DDSRF control II); (<b>d1</b>) <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d2</b>) Second harmonics of <span class="html-italic">U</span><sub>dc</sub> (proposed additional harmonic control); (<b>d3</b>) <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d4</b>) Second harmonics of <span class="html-italic">P</span> (proposed additional harmonic control); (<b>d5</b>) <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d6</b>) Second harmonics of <span class="html-italic">Q</span> (proposed additional harmonic control); (<b>d7</b>) Voltages at the low-voltage side of transformer (proposed additional harmonic control); (<b>d8</b>) Currents at the low-voltage side of transformer (proposed additional harmonic control).</p>
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5527 KiB  
Article
Interaction and Coordination among Nuclear Power Plants, Power Grids and Their Protection Systems
by Guoyang Wu, Ping Ju, Xinli Song, Chenglong Xie and Wuzhi Zhong
Energies 2016, 9(4), 306; https://doi.org/10.3390/en9040306 - 21 Apr 2016
Cited by 14 | Viewed by 7551
Abstract
Nuclear power plants (NPPs) have recently undergone rapid development in China. To improve the performance of both NPPs and grids during adverse conditions, a precise understanding of the coordination between NPPs and grids is required. Therefore, a new mathematical model with reasonable accuracy [...] Read more.
Nuclear power plants (NPPs) have recently undergone rapid development in China. To improve the performance of both NPPs and grids during adverse conditions, a precise understanding of the coordination between NPPs and grids is required. Therefore, a new mathematical model with reasonable accuracy and reduced computational complexity is developed. This model is applicable to the short, mid, and long-term dynamic simulation of large-scale power systems. The effectiveness of the model is verified by using an actual NPP full-scope simulator as a reference. Based on this model, the interaction and coordination between NPPs and grids under the conditions of over-frequency, under-frequency and under-voltage are analyzed, with special stress applied to the effect of protection systems on the safe operation of both NPPs and power grids. Finally, the coordinated control principles and schemes, together with the recommended protection system values, are proposed for both NPPs and grids. These results show that coordination between the protection systems of NPPs and power networks is a crucial factor in ensuring the safe and stable operation of both NPPs and grids. The results can be used as a reference for coordination between NPPs and grids, as well as for parameter optimization of grid-related generator protection of NPPs. Full article
(This article belongs to the Special Issue Electric Power Systems Research)
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<p>The principle chart of a pressurized water reactor (PWR) nuclear power plant (NPP).</p>
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<p>A schematic diagram of the power regulating system.</p>
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<p>A schematic diagram of the temperature regulating system.</p>
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<p>A principle diagram of the steam dump system.</p>
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<p>Hierarchical chart of NPP protection system.</p>
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<p>The single unit infinite system diagram.</p>
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<p>Dynamic responses of NPP G1 during a load ramp decrease. The load request of NPP G1 begins to ramp at 50.0 s from 100.0% full power (FP) to 50.0% FP at a rate of 50 MW/min: (<b>a</b>) Reactor power; (<b>b</b>) turbine power; and (<b>c</b>) coolant average temperature.</p>
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<p>Dynamic responses of NPP G1 during a load step decrease. The load step occurs at 85.0 s from 100.0% to 90.0% nominal power: (<b>a</b>) Reactor power; (<b>b</b>) turbine power; and (<b>c</b>) coolant average temperature.</p>
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<p>Dynamic responses of NPP G1 during a net load rejection. NPP G1 turns to its house load because of the tripping of the high voltage circuit breaker at 15.0 s. The disturbance responses calm down in approximately 1000.0 s: (<b>a</b>) Reactor power; (<b>b</b>) turbine power; and (<b>c</b>) coolant average temperature.</p>
Full article ">Figure 10
<p>Simulation results of a drop in grid frequency. At 85.0 s, the system frequency begins to decrease from 50.0 Hz to 49.8 Hz at a rate of 1 Hz/min: (<b>a</b>) Reactor power; (<b>b</b>) mechanical power and electromagnetic power; (<b>c</b>) average temperature of the primary system; and (<b>d</b>) control valves (CV) opening.</p>
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<p>Simulation results of the rise in grid frequency. At 42.3 s, the system frequency begins to increase from 50.0 Hz to 52.0 Hz at a rate of 1 Hz/min: (<b>a</b>) G bank position; (<b>b</b>) electromagnetic power and mechanical power; (<b>c</b>) reactor power; and (<b>d</b>) CV opening.</p>
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<p>Simulation results of a drop in grid voltage. At 23.8 s, the grid voltage begins to decrease from 550.0 kV to 450.0 kV at a rate of 100 kV/min: (<b>a</b>) Generator voltage; and (<b>b</b>) reactor power.</p>
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<p>Network diagram of the power systems case.</p>
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<p>Dynamic response curves of NPP G1 in the case of under-frequency. When transmission lines Lin11 and Lin12 are tripped, the local grid become isolated and an active power shortage of 1700 MW occurs. This figure presents the dynamic responses of NPP G1 with different control schemes: (<b>a</b>) Frequency deviation; (<b>b</b>) bus voltage; (<b>c</b>) electromagnetic torque; (<b>d</b>) mechanical torque; (<b>e</b>) coolant pump rotation speed; (<b>f</b>) flow rate; and (<b>g</b>) nuclear power.</p>
Full article ">Figure 14 Cont.
<p>Dynamic response curves of NPP G1 in the case of under-frequency. When transmission lines Lin11 and Lin12 are tripped, the local grid become isolated and an active power shortage of 1700 MW occurs. This figure presents the dynamic responses of NPP G1 with different control schemes: (<b>a</b>) Frequency deviation; (<b>b</b>) bus voltage; (<b>c</b>) electromagnetic torque; (<b>d</b>) mechanical torque; (<b>e</b>) coolant pump rotation speed; (<b>f</b>) flow rate; and (<b>g</b>) nuclear power.</p>
Full article ">Figure 15
<p>Dynamic response curves of NPP G1 in the case of over-frequency. When transmission lines Lin11 and Lin12 are tripped, the local grid is separated from the major grid with a surplus power of approximately 2500 MW. This figure presents the dynamic responses of NPP G1 with different control schemes: (<b>a</b>) Frequency of NPP G1; (<b>b</b>) reactor power of NPP G1; (<b>c</b>) bypass opening; and (<b>d</b>) IV opening.</p>
Full article ">Figure 15 Cont.
<p>Dynamic response curves of NPP G1 in the case of over-frequency. When transmission lines Lin11 and Lin12 are tripped, the local grid is separated from the major grid with a surplus power of approximately 2500 MW. This figure presents the dynamic responses of NPP G1 with different control schemes: (<b>a</b>) Frequency of NPP G1; (<b>b</b>) reactor power of NPP G1; (<b>c</b>) bypass opening; and (<b>d</b>) IV opening.</p>
Full article ">Figure 16
<p>Dynamic response curves of NPP G1 in the case of under-voltage. After an N-2 fault occurs in Lin7 and Lin8, approximately 3300 MW of active power in the local grid is transferred to Lin1, Lin2, Lin3, and Lin4. The reactive power loss increases sharply, and the voltages of some units, such as NPP G1, significantly decrease. This figure presents the dynamic responses of NPP G1 with different control schemes: (<b>a</b>) Field current; (<b>b</b>) bus voltage; (<b>c</b>) electromagnetic torque; (<b>d</b>) mechanical torque; (<b>e</b>) coolant pump rotation speed; (<b>f</b>) coolant flow rate; and (<b>g</b>) nuclear power.</p>
Full article ">Figure 16 Cont.
<p>Dynamic response curves of NPP G1 in the case of under-voltage. After an N-2 fault occurs in Lin7 and Lin8, approximately 3300 MW of active power in the local grid is transferred to Lin1, Lin2, Lin3, and Lin4. The reactive power loss increases sharply, and the voltages of some units, such as NPP G1, significantly decrease. This figure presents the dynamic responses of NPP G1 with different control schemes: (<b>a</b>) Field current; (<b>b</b>) bus voltage; (<b>c</b>) electromagnetic torque; (<b>d</b>) mechanical torque; (<b>e</b>) coolant pump rotation speed; (<b>f</b>) coolant flow rate; and (<b>g</b>) nuclear power.</p>
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1879 KiB  
Article
Parametric Analysis of a Rotary Type Liquid Desiccant Air Conditioning System
by M. Mujahid Rafique, Shafiqur Rehman, Luai M. Alhems and Aref Lashin
Energies 2016, 9(4), 305; https://doi.org/10.3390/en9040305 - 21 Apr 2016
Cited by 12 | Viewed by 6328
Abstract
Now days, air conditioning systems are a must for almost every commercial and residential building to achieve comfortable indoor conditions. The increasing energy demand, and increasing oil prices and pollution levels raise the need for alternative air conditioning systems which can efficiently utilize [...] Read more.
Now days, air conditioning systems are a must for almost every commercial and residential building to achieve comfortable indoor conditions. The increasing energy demand, and increasing oil prices and pollution levels raise the need for alternative air conditioning systems which can efficiently utilize renewable energy resources. The liquid desiccant-based air conditioning method is pollution free and thermal energy-based cooling techniques can use low grade thermal energy resources like solar energy, waste heat, etc. These systems have an additional advantage of cleaning bacteria and fungi from the air. In this paper, a newly proposed rotary liquid desiccant air conditioning system has been investigated theoretically. Most direct contact liquid desiccant cooling systems have the problem of desiccant carryover which can be eliminated using the proposed system. The effects of various key parameters and climatic conditions on the performance of the system have been evaluated. The results showed that if the key parameters of the system are controlled effectively, the proposed cooling system has the ability to achieve the desired supply air conditions. The system can achieve high coefficient of performance (COP) under different conditions. The dehumidifier has a sensible heat ratio (SHR) in the range of 0.3–0.6 for different design, climatic, and operating conditions. The system can remove latent load efficiently in applications which require good humidity control. Full article
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<p>A schematic of the proposed rotary liquid desiccant cooling system.</p>
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<p>Differential control volume for one channel.</p>
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<p>Effect of ambient air temperature on (<b>a</b>) moisture removal rate (<span class="html-italic">M<sub>r</sub></span>); (<b>b</b>) sensible heat ratio (SHR).</p>
Full article ">Figure 3 Cont.
<p>Effect of ambient air temperature on (<b>a</b>) moisture removal rate (<span class="html-italic">M<sub>r</sub></span>); (<b>b</b>) sensible heat ratio (SHR).</p>
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<p>Effect of ambient air humidity ratio on (<b>a</b>) moisture removal rate (<span class="html-italic">M<sub>r</sub></span>); (<b>b</b>) sensible heat ratio (SHR).</p>
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<p>Effect of ambient air humidity ratio on (<b>a</b>) moisture removal rate (<span class="html-italic">M<sub>r</sub></span>); (<b>b</b>) sensible heat ratio (SHR).</p>
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<p>Effect of mass flow rate ratio on the system performance.</p>
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<p>Effect of regeneration temperature on the system performance.</p>
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9376 KiB  
Article
Decreasing NOx of a Low-Speed Two-Stroke Marine Diesel Engine by Using In-Cylinder Emission Control Measures
by Liyan Feng, Jiangping Tian, Wuqiang Long, Weixin Gong, Baoguo Du, Dan Li and Lei Chen
Energies 2016, 9(4), 304; https://doi.org/10.3390/en9040304 - 21 Apr 2016
Cited by 33 | Viewed by 10904
Abstract
The authors applied one-dimensional (1-D) simulation and 3-D Computational Fluid Dynamics (CFD) simulation to evaluate the potential of in-cylinder control methods on a low-speed 2-stroke marine engine to reach the International Maritime Organization (IMO) Tier 3 NOx emissions standards. Reducing the combustion [...] Read more.
The authors applied one-dimensional (1-D) simulation and 3-D Computational Fluid Dynamics (CFD) simulation to evaluate the potential of in-cylinder control methods on a low-speed 2-stroke marine engine to reach the International Maritime Organization (IMO) Tier 3 NOx emissions standards. Reducing the combustion temperature is an important in-cylinder measure to decrease NOx emissions of marine diesel engines. Miller-cycle and Exhaust Gas Recirculation (EGR) are effective methods to reduce the maximum combustion temperature and accordingly decrease NOx emissions. The authors’ calculation results indicate that with a combination of 2-stage turbocharging, a mild Miller-cycle and 10% EGR rate, the NOx emissions can be decreased by 48% without the increased Specific Fuel Oil Consumption (SFOC) penalties; with a medium Miller-cycle and 10% EGR, NOx can be decreased by 56% with a slight increase of SFOC; with a medium Miller-cycle and 20% EGR, NOx can be decreased by 77% and meet IMO Tier 3 standards, but with the high price of a considerable increase of SFOC. The first two schemes are promising to meet IMO Tier 3 standards with good fuel economy if other techniques are combined. Full article
(This article belongs to the Special Issue Combustion and Propulsion)
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Figure 1
<p>The coupled three-dimensional (3-D) Computational Fluid Dynamics (CFD) calculation and 1-D working process calculation for the evaluation of NO<sub>x</sub> reduction of the engine. CAD, Computer Aided Design; CR, Compression Ratio; EGR, Exhaust Gas Recirculation; EVC, Exhaust Valve Closing; NO<sub>x</sub>, nitrogen oxides; SFOC, Specific Fuel Oil Consumption; TC, turbocharger.</p>
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<p>One-dimensional (1-D) working simulation model of the baseline engine.</p>
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<p>Three-dimensional (3-D) Computational Fluid Dynamics (CFD) model of a cylinder or the engine.</p>
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<p>Comparison of measured and calculated cylinder pressure curves.</p>
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<p>Comparison of measured and calculated results of NO<sub>x</sub> emissions.</p>
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<p>Fuel spray at the time of 364 °CA.</p>
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<p>Temperature distribution in cylinder during combustion process.</p>
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<p>NO<sub>x</sub> formation in cylinder.</p>
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<p>Exhaust valve lift curves of the six Miller timings.</p>
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<p>Intake pressure values of different schemes.</p>
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<p>Comparison of pressure curves of different Miller-cycle schemes.</p>
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<p>Comparison of peak local temperatures of different cases.</p>
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<p>Comparison of cylinder temperature distribution of Miller-cycle cases.</p>
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<p>NO<sub>x</sub> distribution of Miller-cycle cases.</p>
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<p>One-dimensional (1-D) simulation model of the engine with 2-stage turbocharger.</p>
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<p>Comparison of (<b>a</b>) the change of Specific Fuel Oil Consumption (SFOC); (<b>b</b>) specific NO<sub>x</sub> emissions and (<b>c</b>) engine power between 1-stage and 2-stage turbocharging.</p>
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<p>One-dimensional (1-D) simulation model of the engine with 2-stage turbocharging and Exhaust Gas Recirculation (EGR) system.</p>
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<p>Comparison of temperature distribution in cylinder for Exhaust Gas Recirculation (EGR) cases. Note: 2-s means 2-stage turbocharging; E5, E10 and E15 mean EGR rates are 5%, 10% and 15% respectively.</p>
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<p>Comparison of NO<sub>x</sub> distribution in cylinder for Exhaust Gas Recirculation (EGR) cases.</p>
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<p>Map of NO<sub>x</sub> and Specific Fuel Oil Consumption (SFOC) changing of the researched cases.</p>
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7714 KiB  
Article
Responses of Ecosystem Service to Land Use Change in Qinghai Province
by Ze Han, Wei Song and Xiangzheng Deng
Energies 2016, 9(4), 303; https://doi.org/10.3390/en9040303 - 21 Apr 2016
Cited by 65 | Viewed by 7035
Abstract
Qinghai Province has a very fragile ecological environment and is an important component of the Qinghai-Tibet Plateau. To understand the disturbance caused by human activities to the local ecological system, it is necessary to evaluate the response of ecological service functions to land [...] Read more.
Qinghai Province has a very fragile ecological environment and is an important component of the Qinghai-Tibet Plateau. To understand the disturbance caused by human activities to the local ecological system, it is necessary to evaluate the response of ecological service functions to land use change in Qinghai Province and to uncover the sensitivity of ecological service functions to land use change. This study uses a proxy-based method and proposes a sensitivity index to describe the degree of ecological service function response to the land use change in Qinghai Province. The findings were as follows. (1) From 1988 to 2008, the area of cultivated land, construction land and water in Qinghai Province increased, and forest land and grassland continuously decreased. The agricultural economy and the development of urbanization are the main driving factors in land use change in this area. Policies and eco-environmental engineering, such as the grain-for-green project, the Three-North shelterbelt project and the natural forest protection project, have certain effects on controlling the expansion of cultivated land. (2) The value of ecosystem services in Qinghai Province was 157.368 billion yuan, 157.149 billion yuan and 157.726 billion yuan in 1988, 2000 and 2008, respectively, decreasing and then increasing again. (3) The average sensitivity index values of ecological services in Qinghai Province for the periods 1988–2000 and 2000–2008 was 0.693 and 1.137, respectively. This means that for every 1% increase in land use change, the ecological service value fluctuated by 0.693% and 1.137% in those periods. Full article
(This article belongs to the Special Issue Large Scale LUCC, Ecosystem Service, Water Balance and Energy Use)
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Figure 1
<p>Geographic position of Qinghai Province.</p>
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<p>Land use situation in Qinghai Province in: (<b>a</b>) 1988, (<b>b</b>) 2000 and (<b>c</b>) 2008.</p>
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<p>Ecological service value per unit area of each town (city) in Qinghai Province in 1988 (yuan/hm<sup>2</sup>).</p>
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<p>Change of ecological service value per unit area of each town (city) in (<b>a</b>) 1988–2000 and (<b>b</b>) 2000–2008 (yuan/hm<sup>2</sup>).</p>
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<p>Sensitivity of ecological service value in Qinghai Province in (<b>a</b>) 1988–2000 and (<b>b</b>) 2000–2008.</p>
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9667 KiB  
Article
Code-to-Code Validation and Application of a Dynamic Simulation Tool for the Building Energy Performance Analysis
by Annamaria Buonomano
Energies 2016, 9(4), 301; https://doi.org/10.3390/en9040301 - 21 Apr 2016
Cited by 34 | Viewed by 7176
Abstract
In this paper details about the results of a code-to-code validation procedure of an in-house developed building simulation model, called DETECt, are reported. The tool was developed for research purposes in order to carry out dynamic building energy performance and parametric analyses by [...] Read more.
In this paper details about the results of a code-to-code validation procedure of an in-house developed building simulation model, called DETECt, are reported. The tool was developed for research purposes in order to carry out dynamic building energy performance and parametric analyses by taking into account new building envelope integrated technologies, novel construction materials and innovative energy saving strategies. The reliability and accuracy of DETECt was appropriately tested by means of the standard BESTEST validation procedure. In the paper, details of this validation process are accurately described. A good agreement between the obtained results and all the reference data of the BESTEST qualification cases is achieved. In particular, the obtained results vs. standard BESTEST output are always within the provided ranges of confidence. In addition, several test cases output obtained by DETECt (e.g., dynamic profiles of indoor air and building surfaces temperature and heat fluxes and spatial trends of temperature across walls) are provided. Full article
(This article belongs to the Special Issue Simulation of Polygeneration Systems)
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<p>Simulator scheme and concept.</p>
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<p>Schematics of the resistance capacitance (RC) thermal network.</p>
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<p>South facing window shaded by a horizontal overhang and a vertical fin.</p>
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<p>BESTEST buildings: (<b>a</b>) Cases 600/900; (<b>b</b>) Cases 610/910; (<b>c</b>) Cases 620/920; (<b>d</b>) Cases 630/930.</p>
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<p>Heating annual energy demand for light and heavyweight building qualification tests.</p>
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<p>Cooling annual energy demand for light and heavyweight building qualification tests.</p>
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<p>Heating integrated peak for light and heavyweight building qualification tests.</p>
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<p>Cooling integrated peak for light and heavyweight building qualification tests.</p>
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<p>Hourly free floating temperature: (<b>a</b>) January 4th (cases 600/900 FF), (<b>b</b>) July 27th (cases 650/950 FF).</p>
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<p>Hourly heating and cooling loads: (<b>a</b>) Case 600, (<b>b</b>) Case 900—January 4th.</p>
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<p>BESTEST cases 600 and 900 by switched off HVAC system (Free Floating profiles): DETECt dynamic output for five summer days.</p>
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<p>BESTEST cases 620 and 920 by switched off HVAC system (free floating profiles): DETECt dynamic output for five summer days.</p>
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<p>BESTEST cases 600 and 900 by switched on HVAC system: DETECt dynamic output for five summer days.</p>
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<p>BESTEST cases 620 and 920 by switched on HVAC system: DETECt dynamic output for five summer days.</p>
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<p>BESTEST cases (<b>a</b>) 600 and (<b>b</b>) 900 obtained by switched off HVAC system: Dynamic output for June 12th and spatial temperature gradients across the roof stratification.</p>
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5260 KiB  
Article
Power Quality Improvement and LVRT Capability Enhancement of Wind Farms by Means of an Inductive Filtering Method
by Yanjian Peng, Yong Li, Zhisheng Xu, Ming Wen, Longfu Luo, Yijia Cao and Zbigniew Leonowicz
Energies 2016, 9(4), 302; https://doi.org/10.3390/en9040302 - 20 Apr 2016
Cited by 6 | Viewed by 5127
Abstract
Unlike the traditional method for power quality improvement and low-voltage ride through (LVRT) capability enhancement of wind farms, this paper proposes a new wind power integrated system by means of an inductive filtering method, especially if it contains a grid-connected transformer, a static [...] Read more.
Unlike the traditional method for power quality improvement and low-voltage ride through (LVRT) capability enhancement of wind farms, this paper proposes a new wind power integrated system by means of an inductive filtering method, especially if it contains a grid-connected transformer, a static synchronous compensator (STATCOM) and fully-tuned (FT) branches. First, the main circuit topology of the new wind power integrated system is presented. Then, the mathematical model is established to reveal the mechanism of harmonic suppression and the reactive compensation of the proposed wind power integrated system, and then the realization conditions of the inductive filtering method is obtained. Further, the control strategy of STATCOM is introduced. Based on the measured data for a real wind farm, the simulation studies are carried out to illustrate the performance of the proposed new wind power integrated system. The results indicate that the new system can not only enhance the LVRT capability of wind farms, but also prevent harmonic components flowing into the primary (grid) winding of the grid-connected transformer. Moreover, since the new method can compensate for reactive power in a wind farm, the power factor at the grid side can be improved effectively. Full article
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<p>ENTSO-E low-voltage ride through (LVRT) requirement for grid connection.</p>
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<p>Main circuit topology of proposed wind farm integrated system.</p>
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<p>Wiring scheme of the new grid-connected transformer.</p>
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<p>Power coefficient-tip speed ratio (<span class="html-italic">C</span><sub>p</sub>-<span class="html-italic">λ</span>) curves for different blade angles.</p>
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<p>Fast Fourier transformation results on the output currents of wind farm operating with different wind power ratios.</p>
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<p>Geographical information of the Baolian wind farm.</p>
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<p>The single-phase equivalent-circuit model of the new grid-connected transformer with fully-tuned (FT) branches.</p>
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<p>The current distribution of the new grid-connected transformer.</p>
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<p>Phasor diagram of the voltage and current of the load winding.</p>
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<p>Control strategy of cascade multilevel static synchronous compensator (STATCOM).</p>
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<p>Bode diagram of the quasi-proportional-resonant (QPR) controller.</p>
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<p>Control block of two compensation mode.</p>
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<p>Simulation result about currents in the grid-winding of the new grid-connected transformer when wind farm’s output power is 4.5 MW. (<b>a</b>) No filtering; (<b>b</b>) With inductive filtering.</p>
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<p>Simulation result about currents in the grid winding of the new grid-connected transformer when wind farm’s output power is 15 MW. (<b>a</b>) No filtering; (<b>b</b>) With inductive filtering.</p>
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<p>FFT results on the current waveform in the grid winding. (<b>a</b>) Output power of wind farm is 4.5 MW; (<b>b</b>) Output power of wind farm is 15 MW.</p>
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<p>Wind power data in one day with a three-second interval. (<b>a</b>) Active power; (<b>b</b>) Reactive power.</p>
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<p>Voltage and current in the grid side of the new grid-connected transformer. (<b>a</b>) Without compensation; (<b>b</b>) With compensation.</p>
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<p>A-phase voltage and current of the STATCOM.</p>
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<p>Magnitudes of the voltage at the public network.</p>
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<p>Reactive power injected from STATCOM.</p>
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<p>Output current and command current of the STATCOM.</p>
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5979 KiB  
Article
Preliminary Development of a Free Piston Expander–Linear Generator for Small-Scale Organic Rankine Cycle (ORC) Waste Heat Recovery System
by Gaosheng Li, Hongguang Zhang, Fubin Yang, Songsong Song, Ying Chang, Fei Yu, Jingfu Wang and Baofeng Yao
Energies 2016, 9(4), 300; https://doi.org/10.3390/en9040300 - 20 Apr 2016
Cited by 47 | Viewed by 10549
Abstract
A novel free piston expander-linear generator (FPE-LG) integrated unit was proposed to recover waste heat efficiently from vehicle engine. This integrated unit can be used in a small-scale Organic Rankine Cycle (ORC) system and can directly convert the thermodynamic energy of working fluid [...] Read more.
A novel free piston expander-linear generator (FPE-LG) integrated unit was proposed to recover waste heat efficiently from vehicle engine. This integrated unit can be used in a small-scale Organic Rankine Cycle (ORC) system and can directly convert the thermodynamic energy of working fluid into electric energy. The conceptual design of the free piston expander (FPE) was introduced and discussed. A cam plate and the corresponding valve train were used to control the inlet and outlet valve timing of the FPE. The working principle of the FPE-LG was proven to be feasible using an air test rig. The indicated efficiency of the FPE was obtained from the pV indicator diagram. The dynamic characteristics of the in-cylinder flow field during the intake and exhaust processes of the FPE were analyzed based on Fluent software and 3D numerical simulation models using a computation fluid dynamics method. Results show that the indicated efficiency of the FPE can reach 66.2% and the maximal electric power output of the FPE-LG can reach 22.7 W when the working frequency is 3 Hz and intake pressure is 0.2 MPa. Two large-scale vortices are formed during the intake process because of the non-uniform distribution of velocity and pressure. The vortex flow will convert pressure energy and kinetic energy into thermodynamic energy for the working fluid, which weakens the power capacity of the working fluid. Full article
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Graphical abstract

Graphical abstract
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<p>Prototype and 3D model of the free piston expander-linear generator (FPE-LG): (<b>a</b>) FPE-LG prototype; (<b>b</b>) 3D model of the FPE-LG; (<b>c</b>) Cross-sectional view of the free piston expander (FPE).</p>
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<p>Schematic diagram of small-scale Organic Rankine Cycle (ORC) system using FPE-LG.</p>
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<p>Exploded view of the FPE configuration (small fasteners are omitted for clarity).</p>
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<p>Working process of FPE and corresponding behaviors of the valve train: (<b>a</b>) initial position; (<b>b</b>) intake process; (<b>c</b>) expansion process; (<b>d</b>) exhaust process.</p>
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<p>Air test rig of the FPE-LG: (<b>a</b>) the schematic diagram of experimental system; (<b>b</b>) the picture of experimental system.</p>
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<p>Computational domains of the FPE.</p>
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<p>Vortex flow in cylinder during the intake process: (<b>a</b>) Main view; (<b>b</b>) View 1: cross-section vertical to the inlet valve axis, 10 mm below the inlet valve; (<b>c</b>) View 2: cross-section vertical to the inlet valve axis, 50 mm below the inlet valve; (<b>d</b>) View 3: longitudinal-section along the valve axes.</p>
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<p>Velocity distribution and streamlines during the intake process: (<b>a</b>) θ = 2°; (<b>b</b>) θ = 10°; (<b>c</b>) θ = 14°; (<b>d</b>) θ = 45°; (<b>e</b>) θ = 75°; (<b>f</b>) θ = 88°.</p>
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<p>Velocity distribution during the exhaust process: (<b>a</b>) θ = 185°; (<b>b</b>) θ = 245°; (<b>c</b>) θ = 275°.</p>
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<p>Variation of in-cylinder pressure and piston displacement of the FPE A: (<b>a</b>) 1.5 Hz; (<b>b</b>) 2 Hz; (<b>c</b>) 2.5 Hz; (<b>d</b>) 3 Hz.</p>
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<p><span class="html-italic">p</span>–<span class="html-italic">V</span> indicator diagram of the FPE A.</p>
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<p><span class="html-italic">p</span>–<span class="html-italic">V</span> diagram and indicated efficiency for different intake pressures: (<b>a</b>) <span class="html-italic">p</span>–<span class="html-italic">V</span> diagram for different intake pressures; (<b>b</b>) indicated efficiency and energy losses.</p>
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<p>Electric power output of FPE-LG.</p>
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7421 KiB  
Article
Numerical Study on the Formation of Shear Fracture Network
by Zhaobin Zhang and Xiao Li
Energies 2016, 9(4), 299; https://doi.org/10.3390/en9040299 - 20 Apr 2016
Cited by 14 | Viewed by 5625
Abstract
Shear fracture network is important to the hydraulic fracturing treatment of a shale gas reservoir. In this paper, the formation of shear fracture network is investigated by a Displacement Discontinuity Method (DDM) based model. The results show that the sliding of fracture surface [...] Read more.
Shear fracture network is important to the hydraulic fracturing treatment of a shale gas reservoir. In this paper, the formation of shear fracture network is investigated by a Displacement Discontinuity Method (DDM) based model. The results show that the sliding of fracture surface is irreversible but may change significantly after fluid pressure dissipates. The final sliding distance is different for natural and hydraulic fractures. Most of the shear fractures are natural fractures while the newly formed hydraulic fractures tend to be totally closed after pressure dissipates. The effects of in situ stress are investigated. The affected area reaches its maximum value when the maximum principle stress direction is perpendicular to the principal fracture direction. The effects of the injection rate are also investigated. The increasing of the injection rate is helpful in increasing the fracture aperture, but has no effect on the final sliding distance. Moreover, the effects of the injection rate on the affected area depend on the connectivity of natural fractures. The affected area increases with the injection rate when the connectivity is poor but decreases slightly with injection rate when the connectivity is good. Full article
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<p>The comparison of numerical modelling with analytical solution.</p>
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<p>Two initiation fractures in (<b>a</b>) a horizontal wellbore and (<b>b</b>) two horizontal wellbores. Blue lines represent horizontal wellbores, and red lines represent initial fractures.</p>
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<p>Comparison of propagation paths for initiation fractures in (<b>a</b>) a horizontal wellbore and (<b>b</b>) two horizontal wellbores. The line width represents the fracture aperture.</p>
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<p>The variation of the average sliding distance and the average fracture aperture with time for (<b>a</b>) a single fracture and (<b>b</b>) two intersected fractures. The numerical settings are shown in the sub figures. The maximum principle crustal stress direction is represented by the angle <math display="inline"> <semantics> <mi mathvariant="sans-serif">ψ</mi> </semantics> </math>. The natural fractures are represented by the black segments.</p>
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<p>The numerical setting. The natural fractures are represented by the blue lines. The fluid is injected from the center. The boundary of the simulation region is shown as the red dashed box. The maximum principle stress direction is represented by the angle <math display="inline"> <semantics> <mi mathvariant="sans-serif">ψ</mi> </semantics> </math>.</p>
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<p>The fracture network configurations after fluid injection under different values of crustal stress anisotropy. The fluid pressure is represented by color. The fracture apereture is represented by curve width. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>S</mi> <msub> <mi>A</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>S</mi> <msub> <mi>A</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> ; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>S</mi> <msub> <mi>A</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mn>0.5.</mn> </mrow> </semantics> </math></p>
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<p>The final shear fracture networks after fluid pressure dissipates under different values of crustal stress anisotropy. The gray segments are natural fractures. The blue curves are hydraulic fractures. The shear fractures are colored red. (<b>a</b>) <math display="inline"> <semantics> <mrow> <mi>S</mi> <msub> <mi>A</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mrow> <mi>S</mi> <msub> <mi>A</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> ; (<b>c</b>) <math display="inline"> <semantics> <mrow> <mi>S</mi> <msub> <mi>A</mi> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> <mo>=</mo> <mn>0.5.</mn> </mrow> </semantics> </math></p>
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<p>The variation of (<b>a</b>) the affected area; (<b>b</b>) the fracture density; and (<b>c</b>) the average aperture and sliding distance with stress anisotropy <span class="html-italic">SA<sub>xy</sub></span>. Here, “<span class="html-italic">Final</span>” refers to the values after fluid pressure dissipation.</p>
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<p>The variation of (<b>a</b>) the affected area; (<b>b</b>) the fracture density; and (<b>c</b>) the average aperture and sliding distance with the direction of maximum principle stress. Here, “<span class="html-italic">Final</span>” refers to the values after fluid pressure dissipation.</p>
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<p>The final fracture networks when the connectivity of natural fractures is poor. The gray segments are natural fractures. The blue curves are hydraulic fractures. The shear fractures are colored red. (<b>a</b>) M = 0.058; (<b>b</b>) M = 1.8; (<b>c</b>) M = 58. Here M is the dimensionless fluid viscosity that defined in Equation 16.</p>
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<p>The variation of (<b>a</b>) the affected area; (<b>b</b>) the fracture density; and (<b>c</b>) the averaged displacements with the rate of injection when connectivity of natural fracture is poor. Here, “<span class="html-italic">Final</span>” refers to the values after fluid pressure dissipation.</p>
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<p>The final fracture networks when the connectivity of natural fractures is good. The gray lines are natural fractures. The blue curves are hydraulic fractures. The shear fractures are colored red. (<b>a</b>) M = 0.058; (<b>b</b>) M = 1.8; (<b>c</b>) M = 58. Here M is the dimensionless fluid viscosity that defined in Equation 16.</p>
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<p>The variation of (<b>a</b>) the affected area; (<b>b</b>) the fracture density; and (<b>c</b>) the averaged displacements with the rate of injection when connectivity of natural fracture is good. Here, “<span class="html-italic">Final</span>” refers to the values after fluid pressure dissipation.</p>
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3580 KiB  
Article
Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current
by Wenbiao Li, Longyun Kang, Xiangwei Guo and Yuan Yao
Energies 2016, 9(4), 298; https://doi.org/10.3390/en9040298 - 19 Apr 2016
Cited by 15 | Viewed by 4629
Abstract
Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery [...] Read more.
Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC) balancing. To solve these problems, multi-objective predictive balancing control (MOPBC) based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process. Full article
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<p>The coupling relationship between the internal and external factors.</p>
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<p>Partnership for a new generation of vehicles (PNGV) model.</p>
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<p>Balancing topology.</p>
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<p>The results of state of charge (SOC) under different balancing control conditions. (<b>a</b>) Non-balancing control; (<b>b</b>) SOC balancing control; (<b>c</b>) Multi-objective balancing control; (<b>d</b>) Multi-objective predictive balancing control (MOPBC).</p>
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<p>The results of state of charge (SOC) under different balancing control conditions. (<b>a</b>) Non-balancing control; (<b>b</b>) SOC balancing control; (<b>c</b>) Multi-objective balancing control; (<b>d</b>) Multi-objective predictive balancing control (MOPBC).</p>
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<p>The results of the degradation rate under different balancing control conditions. (<b>a</b>) Non-balancing control; (<b>b</b>) SOC balancing control; (<b>c</b>) Multi-objective balancing control; (<b>d</b>) MOPBC.</p>
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<p>The results of the degradation rate under different balancing control conditions. (<b>a</b>) Non-balancing control; (<b>b</b>) SOC balancing control; (<b>c</b>) Multi-objective balancing control; (<b>d</b>) MOPBC.</p>
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<p>The results of the temperature under different balancing control conditions. (<b>a</b>) Non-balancing control; (<b>b</b>) SOC balancing control; (<b>c</b>) Multi-objective balancing control; (<b>d</b>) MOPBC.</p>
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1827 KiB  
Review
Smart Distribution Systems
by Yazhou Jiang, Chen-Ching Liu and Yin Xu
Energies 2016, 9(4), 297; https://doi.org/10.3390/en9040297 - 19 Apr 2016
Cited by 30 | Viewed by 9754
Abstract
The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The [...] Read more.
The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed. Full article
(This article belongs to the Special Issue Electric Power Systems Research)
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<p>Estimated expenditures of the SGIG programs [<a href="#B14-energies-09-00297" class="html-bibr">14</a>].</p>
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<p>The architecture for integration of smart meters in the distribution operating center.</p>
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<p>Configuration of a simple distribution system.</p>
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<p>Temporal Causal Diagram (TCD) for outage management.</p>
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<p>Flowchart of the spanning tree search algorithm.</p>
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<p>One-line diagram of the 4-feeder 1069-node test system.</p>
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<p>Restoration path from WSU generator to critical load in Pullman distribution system.</p>
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5719 KiB  
Article
Decomposition Analysis in Decoupling Transport Output from Carbon Emissions in Guangdong Province, China
by Yalan Zhao, Yaoqiu Kuang and Ningsheng Huang
Energies 2016, 9(4), 295; https://doi.org/10.3390/en9040295 - 19 Apr 2016
Cited by 32 | Viewed by 5650
Abstract
With a continuously growing share of the world’s overall energy consumption, the transport sector has been acknowledged as one of the most important contributors to global carbon emissions. This paper applies a complete decomposition and decoupling analysis to investigate and quantitatively analyze the [...] Read more.
With a continuously growing share of the world’s overall energy consumption, the transport sector has been acknowledged as one of the most important contributors to global carbon emissions. This paper applies a complete decomposition and decoupling analysis to investigate and quantitatively analyze the main factors influencing the energy-related carbon emissions of the transport (TCE) sector during 1995–2012 in Guangdong, the richest and most populated province in China. Results showed that decoupling level between transport output and TCE was relatively low, especially when compared with year 1995, in which case it remained as expansive coupling. Optimization of tertiary industry structure was the main factor inhibiting TCE increase. However the rapid growth of GDP per capita and population was more powerful at boosting TCE, resulting in elasticity index rising directly. 2005 was a turning point when environmental friendly policies took action, after which decoupling state improved significantly, achieving weak decoupling when comparing adjacent years. By studying TCE and its components, we found that the National 5-Year Plan policy impacts TCE tremendously, which leads to a 5-year periodic pattern of fluctuations. This highlights policy as potentially the most important factor behind Guangdong’s decoupling effort, dwarfing the impact from energy and other inner-drivers. Full article
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<p>Carbon emissions and its average annual growth rate in Guangdong Province during 1995–2012.</p>
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<p>Energy intensity of transport sector, three main industries and the grand total in Guangdong.</p>
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<p>Average TCE coefficient and TCE structure of different energy types.</p>
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<p>Decoupling elasticity index and its components of the transport sector in Guangdong, in the initial view (<b>a</b>) and alternate view (<b>b</b>).</p>
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<p>Decoupling effort and its components of the transport sector in Guangdong, in the initial view (<b>a</b>) and alternate view (<b>b</b>).</p>
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<p>Industrial Structure in Guangdong Province.</p>
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<p>Proportions of the transport sector in tertiary industry of Guangdong, weighted by output, energy consumption, CE and energy intensity.</p>
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<p>The variation which reflected the five-year periodic pattern: (<b>a</b>) The growth of GDP, GDPPC, TO and SO; (<b>b</b>) Proportions of the transport sector in tertiary industry, weighted by output (TS) and energy intensity; (<b>c</b>) Growth of transport energy consumption and TCE; (<b>d</b>) Transport energy intensity and average TCE coefficient; (<b>e</b>) Decoupling index in initial view; (<b>f</b>) Decoupling index in alternate view; (<b>g</b>) Decoupling effort in initial view; (<b>h</b>) Decoupling effort in alternate view.</p>
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<p>The variation which reflected the five-year periodic pattern: (<b>a</b>) The growth of GDP, GDPPC, TO and SO; (<b>b</b>) Proportions of the transport sector in tertiary industry, weighted by output (TS) and energy intensity; (<b>c</b>) Growth of transport energy consumption and TCE; (<b>d</b>) Transport energy intensity and average TCE coefficient; (<b>e</b>) Decoupling index in initial view; (<b>f</b>) Decoupling index in alternate view; (<b>g</b>) Decoupling effort in initial view; (<b>h</b>) Decoupling effort in alternate view.</p>
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<p>The variation which reflected the five-year periodic pattern: (<b>a</b>) The growth of GDP, GDPPC, TO and SO; (<b>b</b>) Proportions of the transport sector in tertiary industry, weighted by output (TS) and energy intensity; (<b>c</b>) Growth of transport energy consumption and TCE; (<b>d</b>) Transport energy intensity and average TCE coefficient; (<b>e</b>) Decoupling index in initial view; (<b>f</b>) Decoupling index in alternate view; (<b>g</b>) Decoupling effort in initial view; (<b>h</b>) Decoupling effort in alternate view.</p>
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<p>The variation which reflected the five-year periodic pattern: (<b>a</b>) The growth of GDP, GDPPC, TO and SO; (<b>b</b>) Proportions of the transport sector in tertiary industry, weighted by output (TS) and energy intensity; (<b>c</b>) Growth of transport energy consumption and TCE; (<b>d</b>) Transport energy intensity and average TCE coefficient; (<b>e</b>) Decoupling index in initial view; (<b>f</b>) Decoupling index in alternate view; (<b>g</b>) Decoupling effort in initial view; (<b>h</b>) Decoupling effort in alternate view.</p>
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1896 KiB  
Article
How Can the Context Affect Policy Decision-Making: The Case of Climate Change Mitigation Policies in the Greek Building Sector
by Niki-Artemis Spyridaki, Anastasia Ioannou and Alexandros Flamos
Energies 2016, 9(4), 294; https://doi.org/10.3390/en9040294 - 18 Apr 2016
Cited by 8 | Viewed by 5821
Abstract
The influence of context dynamics in the course of the climate change mitigation policy instruments’ (PIs) deployment cycle, usually causes a need for policy adaptation mechanisms to ensure that policies can meet the sector needs efficiently and effectively. In this paper, we argue [...] Read more.
The influence of context dynamics in the course of the climate change mitigation policy instruments’ (PIs) deployment cycle, usually causes a need for policy adaptation mechanisms to ensure that policies can meet the sector needs efficiently and effectively. In this paper, we argue that important contextual factors are the ones that are perceived to have a great impact over policy effectiveness by key related actors. By examining more thoroughly those effects over PIs, as perceived by policy and market actors, useful feedback on observed policy adaptations can be highlighted. In this context, the aim of this paper is to present a conceptual framework which seeks to investigate the impact of key external factors on policy decision-making. This framework is then applied to policies intended to foster sustainability in the Greek building sector. Contextual parameters that are influential over the effectiveness of the national energy conservation measures are identified through a stakeholder survey. Cluster analysis is then employed for the elicitation of three distinct decision-making priorities’ scenarios. General macroeconomic trends, energy costs, characteristics of the building sector and socio-institutional factors are prioritized differently from various types of actors and induce certain types of PI changes. Distinguishing among the different types of PI change can help explain better under which contextual circumstances policy adaptations occur and provide guidance to other policy makers when found in similar decisional contexts. Full article
(This article belongs to the Special Issue Energy Policy and Climate Change 2016)
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<p>The research framework.</p>
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<p>Dendogram of clusters according to group weights. Group composition as delineated by the cutting line B-B’: Group 1: RES market actors, RES service (MEECC), Energy Efficiency Division (CRES), Energy Inspectorate (MEECC) RES/EE installers, Group 2: Representatives from the ΕΥSED ΕΝ/ΚΑ Department (MEECC) Group 3: Representatives from Environmental Companies, ESCO, the Energy efficiency Division (CRES) and Department of Energy Policy and Planning (CRES) and, Building Owners’ Association.</p>
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<p>Number of building permits for new buildings per year, from 2006 to 2012 in Greece (Source: [<a href="#B50-energies-09-00294" class="html-bibr">50</a>]).</p>
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<p>Distribution of EPCs by Energy Class (Source: [<a href="#B53-energies-09-00294" class="html-bibr">53</a>]). The energy performance of buildings is classified according to predetermined energy label classes starting from A+ which corresponds to the most efficient classification, A, B+, B, Γ, Δ, E, Z to H corresponding to the most energy intensive building category.</p>
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3716 KiB  
Article
Impact of Biodiesel Blends and Di-Ethyl-Ether on the Cold Starting Performance of a Compression Ignition Engine
by Adrian Clenci, Rodica Niculescu, Amélie Danlos, Victor Iorga-Simăn and Alina Trică
Energies 2016, 9(4), 284; https://doi.org/10.3390/en9040284 - 18 Apr 2016
Cited by 23 | Viewed by 6639
Abstract
The use of biodiesel fuel in compression ignition engines has the potential to reduce CO2, which can lead to a reduction in global warming and environmental hazards. Biodiesel is an attractive fuel, as it is made from renewable resources. Many studies [...] Read more.
The use of biodiesel fuel in compression ignition engines has the potential to reduce CO2, which can lead to a reduction in global warming and environmental hazards. Biodiesel is an attractive fuel, as it is made from renewable resources. Many studies have been conducted to assess the impact of biodiesel use on engine performances. Most of them were carried out in positive temperature conditions. A major drawback associated with the use of biodiesel, however, is its poor cold flow properties, which have a direct influence on the cold starting performance of the engine. Since diesel engine behavior at negative temperatures is an important quality criterion of the engine’s operation, one goal of this paper is to assess the starting performance at −20 °C of a common automotive compression ignition engine, fueled with different blends of fossil diesel fuel and biodiesel. Results showed that increasing the biodiesel blend ratio generated a great deterioration in engine startability. Another goal of this study was to determine the biodiesel blend ratio limit at which the engine would not start at −20 °C and, subsequently, to investigate the impact of Di-Ethyl-Ether (DEE) injection into the intake duct on the engine’s startability, which was found to be recovered. Full article
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<p>Engine behavior during cold start.</p>
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<p>Battery voltage evolution.</p>
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<p>Absorbed current intensity evolution.</p>
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<p>Engine speed evolution.</p>
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<p>Speed-related parameters used to assess the engine cold starting performance: (<b>a</b>) speed evolution over time; (<b>b</b>) specific speeds.</p>
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<p>Evolution of the mass of injected fuel.</p>
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<p>Succession of Engine Cycles 5–8: (<b>a</b>) Diesel fuel <span class="html-italic">vs.</span> B30; (<b>b</b>) Diesel fuel <span class="html-italic">vs.</span> B50_DEE.</p>
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<p>Pressure peaks’ cyclic evolution.</p>
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<p>Misfire engine cycles.</p>
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<p>Cyclic dispersion over the first 200 engine cycles.</p>
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<p>Cycle-to-cycle analysis during the idling stage.</p>
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10477 KiB  
Article
Aerodynamic Analysis and Three-Dimensional Redesign of a Multi-Stage Axial Flow Compressor
by Tao Ning, Chun-Wei Gu, Wei-Dou Ni, Xiao-Tang Li and Tai-Qiu Liu
Energies 2016, 9(4), 296; https://doi.org/10.3390/en9040296 - 16 Apr 2016
Cited by 8 | Viewed by 8936
Abstract
This paper describes the introduction of three-dimension (3-D) blade designs into a 5-stage axial compressor with multi-stage computational fluid dynamic (CFD) methods. Prior to a redesign, a validation study is conducted for the overall performance and flow details based on full-scale test data, [...] Read more.
This paper describes the introduction of three-dimension (3-D) blade designs into a 5-stage axial compressor with multi-stage computational fluid dynamic (CFD) methods. Prior to a redesign, a validation study is conducted for the overall performance and flow details based on full-scale test data, proving that the multi-stage CFD applied is a relatively reliable tool for the analysis of the follow-up redesign. Furthermore, at the near stall point, the aerodynamic analysis demonstrates that significant separation exists in the last stator, leading to the aerodynamic redesign, which is the focus of the last stator. Multi-stage CFD methods are applied throughout the three-dimensional redesign process for the last stator to explore their aerodynamic improvement potential. An unconventional asymmetric bow configuration incorporated with leading edge re-camber and re-solidity is employed to reduce the high loss region dominated by the mainstream. The final redesigned version produces a 13% increase in the stall margin while maintaining the efficiency at the design point. Full article
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<p>Overall configuration of the compressor.</p>
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<p>Typical mesh topology.</p>
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<p>Mesh for the CFD computations.</p>
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<p>Measured and predicted overall performance. NS: Near Stall.</p>
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<p>Measured and predicted casing static pressure. (<b>a</b>) Design point; (<b>b</b>) Near stall point. Exp.: Experiment.</p>
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<p>Measured and predicted total pressure profiles at the leading edges of stator 4 and stator 5. (<b>a</b>) Design point of stator 4 and stator 5; (<b>b</b>) Near stall point of stator 4 and stator 5.</p>
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<p>Limiting streamlines on the suction side of each blade at the near stall point.</p>
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<p>Streamwise diffusion factor distributions of each row.</p>
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<p>Surface static pressure rise coefficient distributions at the 20%, 50% and 80% spans of stator 5.</p>
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<p>Iso-surface of the axial velocity (−1 m/s) for stator 5 of the baseline (near the stall points). TE.: trailing edge; LE.: leading edge.</p>
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<p>Stacking lines sketch and blade shapes of stator 5. <span class="html-italic">SS</span>: suction side; <span class="html-italic">PS</span>: pressure side.</p>
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<p>Spanwise distributions of the inlet axial velocity (near the stall points).</p>
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<p>Iso-surface of the axial velocity (−1 m/s) for the bowed stator 5 (near the stall points).</p>
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<p>Relative Mach number contours and streamlines at the 35% span of stator 5 (near the stall points).</p>
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<p>Spanwise distributions of the incidence at the design point.</p>
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<p>Inlet blade angle and solidity profiles of the baseline and redesigned stator 5.</p>
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<p>Spanwise distributions of the incidence for the baseline and redesigned stator 5 (near the stall points).</p>
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<p>Loss profiles of the baseline and redesigned stator 5 (near the stall points).</p>
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<p>Total pressure contours downstream of the baseline and redesigned stator 5 (near the stall points).</p>
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<p>Iso-surface of the axial velocity (−1 m/s) for the baseline and redesigned stator 5 (near the stall points).</p>
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<p>Overall performances.</p>
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6557 KiB  
Article
Influence of Droplet Size on Exergy Destruction in Flow of Concentrated Non-Newtonian Emulsions
by Rajinder Pal
Energies 2016, 9(4), 293; https://doi.org/10.3390/en9040293 - 16 Apr 2016
Cited by 5 | Viewed by 6166
Abstract
The influence of droplet size on exergy destruction rate in flow of highly concentrated oil-in-water emulsions was investigated experimentally in a cone and plate geometry. The oil concentration was fixed at 74.5% by volume. At this dispersed-phase (oil) concentration, two different droplet size [...] Read more.
The influence of droplet size on exergy destruction rate in flow of highly concentrated oil-in-water emulsions was investigated experimentally in a cone and plate geometry. The oil concentration was fixed at 74.5% by volume. At this dispersed-phase (oil) concentration, two different droplet size emulsions were prepared: fine and coarse emulsions. The fine and coarse emulsions were mixed in different proportions to vary the droplet size distribution. Although the dispersed and matrix phases of the emulsions were Newtonian in nature, the emulsions exhibited a non-Newtonian (shear-thinning) behavior due to the high droplet concentration. The shear stress—shear rate data of the emulsions could be described adequately by a power law model. At low shear rates, the exergy destruction rate per unit volume of emulsion exhibited a minimum at a fine emulsion proportion of 35%. The results from the cone and plate geometry were used to simulate exergy loss in pipeline flow of emulsions. The pumping of emulsions becomes more efficient thermodynamically upon mixing of fine and coarse emulsions provided that the flow regime is maintained to be laminar and that the Reynolds number is kept at a low to moderate value. In the turbulent regime, the exergy loss generally increases upon mixing the fine and coarse emulsions. Full article
(This article belongs to the Special Issue Exergy Analysis of Energy Systems)
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<p>Schematic diagram of a cone-and-plate viscometer.</p>
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<p>Photomicrographs of fine (<b>a</b>) and coarse (<b>b</b>) O/W emulsions.</p>
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<p>Comparison of droplet size distributions of fine and coarse O/W emulsions.</p>
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<p>Shear stress <span class="html-italic">versus</span> shear rate plots for mixtures of fine and coarse O/W emulsions. In the figure “f” refers to proportion of fine emulsion and “c” refers to proportion of coarse emulsion.</p>
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<p>Power law parameters (<span class="html-italic">K</span> and <span class="html-italic">n</span>) with error bars for mixtures of fine and coarse O/W emulsions.</p>
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<p>Comparison of viscosity data of fine and coarse emulsions obtained from different instruments.</p>
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<p>Exergy destruction rate <span class="html-italic">versus</span> shear stress for mixtures of fine and coarse O/W emulsions.</p>
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<p>Exergy destruction rate <span class="html-italic">versus</span> shear rate for mixtures of fine and coarse O/W emulsions.</p>
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<p>Shear rate and exergy destruction rate for mixtures of fine and coarse O/W emulsions at a fixed shear stress of 0.5 Pa.</p>
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<p>Exergy destruction rate per unit volume for mixtures of fine and coarse O/W emulsions at a fixed shear rate of 5 s<sup>−1</sup>.</p>
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<p>Exergy destruction rate per unit pipe length in adiabatic laminar flow of mixtures of fine and coarse O/W emulsions.</p>
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<p>The effect of Re_<span class="html-italic">n</span> on exergy loss in adiabatic pipeline flow of mixtures of fine and coarse O/W emulsions.</p>
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<p>Exergy destruction rate per unit pipe length in adiabatic turbulent flow of mixtures of fine and coarse O/W emulsions.</p>
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<p>Exergy destruction rate in adiabatic turbulent flow of mixtures of fine and coarse O/W emulsions at a fixed Re_<span class="html-italic">n</span> of 85,223.</p>
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5212 KiB  
Article
Modeling and Simulation of the Thermal Runaway Behavior of Cylindrical Li-Ion Cells—Computing of Critical Parameters
by Andreas Melcher, Carlos Ziebert, Magnus Rohde and Hans Jürgen Seifert
Energies 2016, 9(4), 292; https://doi.org/10.3390/en9040292 - 16 Apr 2016
Cited by 69 | Viewed by 11670
Abstract
The thermal behavior of Li-ion cells is an important safety issue and has to be known under varying thermal conditions. The main objective of this work is to gain a better understanding of the temperature increase within the cell considering different heat sources [...] Read more.
The thermal behavior of Li-ion cells is an important safety issue and has to be known under varying thermal conditions. The main objective of this work is to gain a better understanding of the temperature increase within the cell considering different heat sources under specified working conditions. With respect to the governing physical parameters, the major aim is to find out under which thermal conditions a so called Thermal Runaway occurs. Therefore, a mathematical electrochemical-thermal model based on the Newman model has been extended with a simple combustion model from reaction kinetics including various types of heat sources assumed to be based on an Arrhenius law. This model was realized in COMSOL Multiphysics modeling software. First simulations were performed for a cylindrical 18650 cell with a L i C o O 2 -cathode to calculate the temperature increase under two simple electric load profiles and to compute critical system parameters. It has been found that the critical cell temperature T crit , above which a thermal runaway may occur is approximately 400 K , which is near the starting temperature of the decomposition of the Solid-Electrolyte-Interface in the anode at 393 . 15 K . Furthermore, it has been found that a thermal runaway can be described in three main stages. Full article
(This article belongs to the Special Issue Electrochemical Energy Storage - 2015)
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<p>Scheme of the several domain levels and their corresponding coordinate systems. (<b>a</b>) Particle domain; (<b>b</b>) electrode domain; (<b>c</b>) cell domain.</p>
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<p>(<b>a</b>) First <math display="inline"> <semantics> <mrow> <mn>1000</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics> </math> of the current load profile I without relaxation times and (<b>b</b>) of the current load profile II with relaxation times of 250 s between charging and discharging pulses.</p>
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<p>Meshing of a cylindrical 18650 cell.</p>
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<p><math display="inline"> <semantics> <mrow> <mi>t</mi> <mo>-</mo> <mover> <mi>T</mi> <mo>¯</mo> </mover> </mrow> </semantics> </math>-plot (<b>a</b>–<b>c</b>) and corresponding <math display="inline"> <semantics> <mrow> <mover> <mi>T</mi> <mo>¯</mo> </mover> <mo>-</mo> <mtext>d</mtext> <mover> <mi>T</mi> <mo>¯</mo> </mover> <mo>/</mo> <mtext>d</mtext> <mi>t</mi> </mrow> </semantics> </math> plot (<b>d</b>–<b>f</b>) for the <span class="html-italic">Model A</span> (blue) and <span class="html-italic">Model B</span> (red) and three different <span class="html-italic">h</span> values.</p>
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<p>Thermal runaway time <math display="inline"> <semantics> <msub> <mi>t</mi> <mtext>TR</mtext> </msub> </semantics> </math> as function of the convective heat transfer coefficient <span class="html-italic">h</span> (<b>a</b>),(<b>b</b>) and as function of the environmental temperature <math display="inline"> <semantics> <msub> <mi>T</mi> <mtext>env</mtext> </msub> </semantics> </math> (<b>c</b>),(<b>d</b>) for both current profiles <span class="html-italic">Profile (I)</span> and <span class="html-italic">Profile (II)</span>.</p>
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<p>Thermal runaway time <math display="inline"> <semantics> <msub> <mi>t</mi> <mtext>TR</mtext> </msub> </semantics> </math> as function of the convective heat transfer coefficient <span class="html-italic">h</span> (<b>a</b>),(<b>b</b>) and as function of the environmental temperature <math display="inline"> <semantics> <msub> <mi>T</mi> <mtext>env</mtext> </msub> </semantics> </math> (<b>c</b>),(<b>d</b>) for both current profiles <span class="html-italic">Profile (I)</span> and <span class="html-italic">Profile (II)</span>.</p>
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<p><span class="html-italic">Profile (I)</span>: Temperature profile for the mean cell temperature <math display="inline"> <semantics> <mover> <mi>T</mi> <mo>¯</mo> </mover> </semantics> </math>: (<b>a</b>) <math display="inline"> <semantics> <msub> <mi>T</mi> <mtext>env</mtext> </msub> </semantics> </math> fixed, <span class="html-italic">h</span> varied; (<b>b</b>) <span class="html-italic">h</span> fixed, <math display="inline"> <semantics> <msub> <mi>T</mi> <mtext>env</mtext> </msub> </semantics> </math> varied.</p>
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<p><span class="html-italic">Profile (I)</span>: Temperature profile for the mean cell temperature <math display="inline"> <semantics> <mover> <mi>T</mi> <mo>¯</mo> </mover> </semantics> </math>: (<b>a</b>) <math display="inline"> <semantics> <msub> <mi>T</mi> <mtext>env</mtext> </msub> </semantics> </math> fixed, <span class="html-italic">h</span> varied; (<b>b</b>) <span class="html-italic">h</span> fixed, <math display="inline"> <semantics> <msub> <mi>T</mi> <mtext>env</mtext> </msub> </semantics> </math> varied.</p>
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4603 KiB  
Article
Study on the System Design of a Solar Assisted Ground Heat Pump System Using Dynamic Simulation
by Min Gyung Yu, Yujin Nam, Youngdong Yu and Janghoo Seo
Energies 2016, 9(4), 291; https://doi.org/10.3390/en9040291 - 16 Apr 2016
Cited by 12 | Viewed by 7142
Abstract
Recently, the use of hybrid systems using multiple heat sources in buildings to ensure a stable energy supply and improve the system performance has gained attention. Among them, a heat pump system using both solar and ground heat was developed and various system [...] Read more.
Recently, the use of hybrid systems using multiple heat sources in buildings to ensure a stable energy supply and improve the system performance has gained attention. Among them, a heat pump system using both solar and ground heat was developed and various system configurations have been introduced. However, establishing a suitable design method for the solar-assisted ground heat pump (SAGHP) system including a thermal storage tank is complicated and there are few quantitative studies on the detailed system configurations. Therefore, this study developed three SAGHP system design methods considering the design factors focused on the thermal storage tank. Using dynamic energy simulation code (TRNSYS 17), individual performance analysis models were developed and long-term quantitative analysis was carried out to suggest optimum design and operation methods. As a result, it was found that SYSTEM 2 which is a hybrid system with heat storage tank for only a solar system showed the highest average heat source temperature of 14.81 °C, which is about 11 °C higher than minimum temperature in SYSTEM 3. Furthermore, the best coefficient of performance (COP) values of heat pump and system were 5.23 and 4.32 in SYSYEM 2, using high and stable solar heat from a thermal storage tank. Moreover, this paper considered five different geographical and climatic locations and the SAGHP system worked efficiently in having high solar radiation and cool climate zones and the system COP was 4.51 in the case of Winnipeg (Canada) where the highest heating demand is required. Full article
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<p>Schematic diagram of the solar-assisted ground heat pump system (SAGHP) [<a href="#B14-energies-09-00291" class="html-bibr">14</a>].</p>
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<p>Conceptual diagram of systems [<a href="#B15-energies-09-00291" class="html-bibr">15</a>]. (<b>A</b>) SYSTEM 1 [<a href="#B14-energies-09-00291" class="html-bibr">14</a>]; (<b>B</b>) SYSTEM 2; (<b>C</b>) SYSTEM 3.</p>
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<p>Performance curve of the heat pump.</p>
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<p>Simulation result of the representative day (13th January). (<b>A</b>,<b>B</b>) SYSTEM 1; (<b>C</b>,<b>D</b>) SYSTEM 2; (<b>E</b>,<b>F</b>) SYSTEM 3; (<b>A</b>,<b>C</b>,<b>E</b>) heat source temp. and heat pump coefficient of performance (COP); (<b>B</b>,<b>D</b>,<b>F</b>) production and system COP.</p>
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<p>Energy production and system COP in case. (<b>A</b>) SYSTEM 1; (<b>B</b>) SYSTEM 2; (<b>C</b>) SYSTEM 3.</p>
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<p>Simulation results of heat pump in case. (<b>A</b>,<b>C</b>,<b>E</b>) Ground temperature; (<b>B</b>,<b>D</b>,<b>F</b>) heat pump inlet temp. and COP; (<b>A</b>,<b>B</b>) SYSTEM 1; (<b>C</b>,<b>D</b>) SYSTEM 2; (<b>E</b>,<b>F</b>) SYSTEM 3.</p>
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<p>Solar heat production and COP according to the control temperature in SYSTEM 2. (<b>A</b>) Day; (<b>B</b>) Night.</p>
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<p>Solar heat production and COP according to the control temperature in SYSTEM 3. (<b>A</b>) Day; (<b>B</b>) Night.</p>
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<p>Life cycle cost results (All day, Seoul).</p>
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<p>Operation cost calculation according to five different climate locations. (<b>A</b>) Day; (<b>B</b>) Night.</p>
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4413 KiB  
Article
Robust Longitudinal Speed Control of Hybrid Electric Vehicles with a Two-Degree-of-Freedom Fuzzy Logic Controller
by Jau-Woei Perng and Yi-Horng Lai
Energies 2016, 9(4), 290; https://doi.org/10.3390/en9040290 - 16 Apr 2016
Cited by 11 | Viewed by 5827
Abstract
This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and [...] Read more.
This paper proposes a new robust two-degree-of-freedom (DoF) design method for controlling the nonlinear longitudinal speed problem of hybrid electric vehicles (HEVs). First, the uncertain parameters of the HEV model are described by fuzzy α-cut representation, in which the interval uncertainty and the possibility can be simultaneously indicated by the fuzzy membership function. For the fuzzy parametric uncertain system, the maximum uncertainty interval can be translated into the weighting matrix Q of the linear quadratic tracking problem to guarantee that the designed feedback controller is robust. Second, the fuzzy forward compensator is incorporated with a robust feedback controller to enhance the system tracking response. The simulation results demonstrate that the proposed controller has higher tracking performance compared to the single-DoF self-tuning fuzzy logic controller or conventional optimal H controller. Full article
(This article belongs to the Special Issue Power Management for Hybrids and Vehicle Drivetrains)
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<p>The speed control scheme of the HEV with an electronic throttle control system (ETCS) and a nonlinear vehicle dynamic system.</p>
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<p>Fuzzy <math display="inline"> <semantics> <mi>α</mi> </semantics> </math>-cut representation of the uncertain parameter.</p>
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<p>Membership function for <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mtext>p</mtext> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mtext>q</mtext> <mo stretchy="false">˜</mo> </mover> <mn>0</mn> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mtext>q</mtext> <mo stretchy="false">˜</mo> </mover> <mn>1</mn> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mtext>q</mtext> <mo stretchy="false">˜</mo> </mover> <mn>2</mn> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mtext>q</mtext> <mo stretchy="false">˜</mo> </mover> <mn>3</mn> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mtext>q</mtext> <mo stretchy="false">˜</mo> </mover> <mn>4</mn> </msub> </mrow> </semantics> </math> .</p>
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<p>Block diagram for the HEV longitudinal speed control with robust feedback controller.</p>
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<p>Block diagram for the HEV longitudinal speed control with forward compensator.</p>
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<p>Block diagram for the HEV longitudinal speed control with the 2-DoF fuzzy controller. FLC, fuzzy logic controller.</p>
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<p>Step response for the HEV speed control (<b>a</b>) with the <math display="inline"> <semantics> <mrow> <msub> <mi>H</mi> <mi>∞</mi> </msub> </mrow> </semantics> </math> controller and (<b>b</b>) with the robust feedback controller.</p>
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<p>Wide range cruise tracking response for the HEV speed control (<b>a</b>) with the <math display="inline"> <semantics> <mrow> <msub> <mi>H</mi> <mi>∞</mi> </msub> </mrow> </semantics> </math> controller and (<b>b</b>) with the robust feedback controller.</p>
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<p>Wide range operation response for the HEV speed control (<b>a</b>) with STF-PID and (<b>b</b>) with the 2-DoF robust controller.</p>
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<p>Corresponding change in input energy for the period from t = 5 s to t = 16 s.</p>
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5608 KiB  
Article
High Pressure Oxydesulphurisation of Coal Using KMnO4—Effect of Coal Slurry Concentration, pH and Alkali
by Moinuddin Ghauri, Khurram Shahzad, Abrar Inayat, Zulfiqar Ali and Keith R. Cliffe
Energies 2016, 9(4), 289; https://doi.org/10.3390/en9040289 - 16 Apr 2016
Cited by 6 | Viewed by 5747
Abstract
A high pressure oxydesulphurisation technique was investigated to reduce sulphur content, especially at ambient temperature. Prince of Wales coal was chosen for this study. The focus of the study was on the reduction of both pyritic and organic sulphur. The effects of pressure, [...] Read more.
A high pressure oxydesulphurisation technique was investigated to reduce sulphur content, especially at ambient temperature. Prince of Wales coal was chosen for this study. The focus of the study was on the reduction of both pyritic and organic sulphur. The effects of pressure, coal slurry concentration, pH and KOH concentration in a fixed time interval on sulphur removal were studied with a series of experimental runs at ambient temperature. Heating value recovery was found to be increased with decreased pressure and with increased coal slurry concentration. It was found that sulphur removal was enhanced with an increase in pressure, with a more significant effect on the organic sulphur. With increase in the coal slurry concentration reduction, sulphur was found to be decreased. Full article
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<p>Process flow diagram.</p>
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<p>Effect of pressure on total, pyritic and organic sulphur removal.</p>
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<p>Effect of pressure on heating value recovery of KMnO4 desulphurised coal.</p>
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<p>Effect of temperature on the pyritic sulphur removal.</p>
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<p>Effect of temperature on the organic sulphur removal.</p>
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<p>Effect of temperature on the heating value recovery.</p>
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<p>Effect of coal slurry concentration on pyritic sulphur removal.</p>
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<p>Effect of coal slurry concentration on organic sulphur removal.</p>
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<p>Effect of coal slurry concentration on heating value recovery.</p>
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<p>Effect of pressure on organic sulphur removal.</p>
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<p>Effect of pH on pyritic sulphur removal.</p>
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<p>Effect of pH on organic sulphur removal.</p>
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<p>Effect of pH on heating value recovery.</p>
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<p>Effect of KOH concentration on pyritic sulphur removal.</p>
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<p>Effect of KOH concentration on organic sulphur removal.</p>
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<p>Effect of KOH concentration on heating value recovery.</p>
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7111 KiB  
Article
A High-Performance Adaptive Incremental Conductance MPPT Algorithm for Photovoltaic Systems
by Chendi Li, Yuanrui Chen, Dongbao Zhou, Junfeng Liu and Jun Zeng
Energies 2016, 9(4), 288; https://doi.org/10.3390/en9040288 - 15 Apr 2016
Cited by 65 | Viewed by 11025
Abstract
The output characteristics of photovoltaic (PV) arrays vary with the change of environment, and maximum power point (MPP) tracking (MPPT) techniques are thus employed to extract the peak power from PV arrays. Based on the analysis of existing MPPT methods, a novel incremental [...] Read more.
The output characteristics of photovoltaic (PV) arrays vary with the change of environment, and maximum power point (MPP) tracking (MPPT) techniques are thus employed to extract the peak power from PV arrays. Based on the analysis of existing MPPT methods, a novel incremental conductance (INC) MPPT algorithm is proposed with an adaptive variable step size. The proposed algorithm automatically regulates the step size to track the MPP through a step size adjustment coefficient, and a user predefined constant is unnecessary for the convergence of the MPPT method, thus simplifying the design of the PV system. A tuning method of initial step sizes is also presented, which is derived from the approximate linear relationship between the open-circuit voltage and MPP voltage. Compared with the conventional INC method, the proposed method can achieve faster dynamic response and better steady state performance simultaneously under the conditions of extreme irradiance changes. A Matlab/Simulink model and a 5 kW PV system prototype controlled by a digital signal controller (TMS320F28035) were established. Simulations and experimental results further validate the effectiveness of the proposed method. Full article
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<p>Diagram of the photovoltaic (PV) system.</p>
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<p>Equivalent circuit of PV array.</p>
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<p>Diagram of incremental conductance (INC) maximum power point tracking (MPPT) control unit.</p>
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<p>The proposed maximum power point tracking (MPPT) system.</p>
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<p>Normalized power, abs(<span class="html-italic">dP</span>/<span class="html-italic">dV</span>) under different irradiation levels.</p>
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<p>Normalized <span class="html-italic">V</span>/<span class="html-italic">I</span>, <span class="html-italic">dI</span>/<span class="html-italic">dV</span> and (<span class="html-italic">V</span>/<span class="html-italic">I</span>) × (<span class="html-italic">dI</span>/<span class="html-italic">dV</span>) (p.u.).</p>
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<p>Normalized power, abs(<span class="html-italic">dP/dV</span>) and <span class="html-italic">S</span>(<span class="html-italic">k</span>).</p>
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<p>Adjustment coefficient <span class="html-italic">S</span>(<span class="html-italic">k</span>) <span class="html-italic">versus</span> voltage.</p>
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<p>Flowchart of the proposed algorithm.</p>
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<p>Bode plot of voltage control loop.</p>
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<p>Step response of voltage control loop with proportional integral (PI) compensator.</p>
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<p>(<b>a</b>) Simulation results of fixed step size INC (Δ<span class="html-italic">V<sub>ref</sub></span> = 1 V); (<b>b</b>) Simulation results of fixed step size INC (Δ<span class="html-italic">V<sub>ref</sub></span> = 4.8 V); (<b>c</b>) Simulation results of variable step size INC (<span class="html-italic">N</span> = 1, Δ<span class="html-italic">V<sub>refmax</sub></span> = 4.8 V); (<b>d</b>) Simulation results of proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub> = 4.8 V); (<b>e</b>) Simulation results of proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = 4.8 V, Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub> = 1.6 V).</p>
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<p>The experimental setup.</p>
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<p>The start-up waveforms of INC methods (Time (2 s/div)) (<b>a</b>) Variable step size INC; (<b>b</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>).</p>
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<p>The rise-up situation (Time (1 s/div)) (<b>a</b>) Variable step size INC; (<b>b</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>); (<b>c</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = 3 × Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>).</p>
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<p>The rise-up situation (Time (1 s/div)) (<b>a</b>) Variable step size INC; (<b>b</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>); (<b>c</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = 3 × Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>).</p>
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<p>The decreasing situation (Time (1 s/div)). (<b>a</b>) Variable step size INC; (<b>b</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>); (<b>c</b>) Proposed method (Δ<span class="html-italic">V<sub>ref</sub></span><sub>1</sub> = 3 × Δ<span class="html-italic">V<sub>ref</sub></span><sub>2</sub>).</p>
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629 KiB  
Article
Load Concentration Factor Based Analytical Method for Optimal Placement of Multiple Distribution Generators for Loss Minimization and Voltage Profile Improvement
by Mohsin Shahzad, Ishtiaq Ahmad, Wolfgang Gawlik and Peter Palensky
Energies 2016, 9(4), 287; https://doi.org/10.3390/en9040287 - 14 Apr 2016
Cited by 31 | Viewed by 6711
Abstract
This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced [...] Read more.
This paper presents novel separate methods for finding optimal locations, sizes of multiple distributed generators (DGs) simultaneously and operational power factor in order to minimize power loss and improve the voltage profile in the distribution system. A load concentration factor (LCF) is introduced to select the optimal location(s) for DG placement. Exact loss formula based analytical expressions are derived for calculating the optimal sizes of any number of DGs simultaneously. Since neither optimizing the location nor optimizing the size is done iteratively, like existing methods do, the simulation time is reduced considerably. The exhaustive method is used to find the operational power factor, and it is shown with the results that the losses are further reduced and voltage profile is improved by operating the DGs at operational power factor. Results for power loss reduction and voltage profile improvement in IEEE 37 and 119 node radial distribution systems are presented and compared with the the loss sensitivity factor (LSF) method, improved analytical (IA) and exhaustive load flow method (ELF). The comparison for operational power factor and other power factors is also presented. Full article
(This article belongs to the Special Issue Distributed Renewable Generation)
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<p>Load concentration factors for 37 node system.</p>
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<p>Load concentration factor for 119 node system.</p>
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<p>Line flows for 37 node system.</p>
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<p>Line flows for 119 node system.</p>
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<p>Flowchart of optimal placement.</p>
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<p>IEEE 37 node network.</p>
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<p>IEEE 119 node network.</p>
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<p>Operational power factor for 37 node system.</p>
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<p>Operational power factor for 119 node system.</p>
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<p>Voltage profile for 37 node system with different methods (pf = 0.97).</p>
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<p>Voltage profile for 119 node system with different methods (pf = 0.90).</p>
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1578 KiB  
Article
A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation
by Birgir Hrafnkelsson, Gudmundur V. Oddsson and Runar Unnthorsson
Energies 2016, 9(4), 286; https://doi.org/10.3390/en9040286 - 13 Apr 2016
Cited by 22 | Viewed by 6930
Abstract
A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and [...] Read more.
A novel Monte Carlo (MC) approach is proposed for the simulation of wind speed samples to assess the wind energy production potential of a site. The Monte Carlo approach is based on historical wind speed data and reserves the effect of autocorrelation and seasonality in wind speed observations. No distributional assumptions are made, and this approach is relatively simple in comparison to simulation methods that aim at including the autocorrelation and seasonal effects. Annual energy production (AEP) is simulated by transforming the simulated wind speed values via the power curve of the wind turbine at the site. The proposed Monte Carlo approach is generic and is applicable for all sites provided that a sufficient amount of wind speed data and information on the power curve are available. The simulated AEP values based on the Monte Carlo approach are compared to both actual AEP and to simulated AEP values based on a modified Weibull approach for wind speed simulation using data from the Burfell site in Iceland. The comparison reveals that the simulated AEP values based on the proposed Monte Carlo approach have a distribution that is in close agreement with actual AEP from two test wind turbines at the Burfell site, while the simulated AEP of the Weibull approach is such that the P50 and the scale are substantially lower and the P90 is higher. Thus, the Weibull approach yields AEP that is not in line with the actual variability in AEP, while the Monte Carlo approach gives a realistic estimate of the distribution of AEP. Full article
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<p>Quantile-Quantile Plot (QQ-plot) of measured data at 55 m <span class="html-italic">vs</span>. data measured at 10 m extrapolated to a 55 m height.</p>
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<p>Measured power curve <span class="html-italic">vs.</span> estimated curve for the E44 900 kW turbine.</p>
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<p>Autocorrelation plots for the twelve months of the year with lag measured in days.</p>
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<p>Sample probability density functions (pdfs) based on the observed wind speed at Burfell and samples of the Weibull wind speed simulation.</p>
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<p>pdf plot of MC simulated wind at Burfell.</p>
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<p>pdfs of MC simulated AEP (dashed curve) and Weibull simulated AEP (dash-dot curve) for a E44 900 kW wind turbine (same type as Turbines 1 and 2 at Burfell).</p>
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<p>cdfs of MC-simulated AEP (dashed curve) and Weibull-simulated AEP (dash-dot curve) for a E44 900 kW wind turbine and the observed AEP (solid curve) at Burfell for the same type of turbine (Turbine 1).</p>
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5179 KiB  
Article
Optimal Design of an Axial-Flux Permanent-Magnet Motor for an Electric Vehicle Based on Driving Scenarios
by Yee Pien Yang and Guan Yu Shih
Energies 2016, 9(4), 285; https://doi.org/10.3390/en9040285 - 13 Apr 2016
Cited by 30 | Viewed by 15530
Abstract
This paper proposes a driving-scenario oriented optimal design of an axial-flux permanent-magnet (AFPM) motor for an electric vehicle. The target torque and speed (TN) curve is defined as three operation zones-constant torque, maximum direct current, and maximum voltage—based on the driving scenario. The [...] Read more.
This paper proposes a driving-scenario oriented optimal design of an axial-flux permanent-magnet (AFPM) motor for an electric vehicle. The target torque and speed (TN) curve is defined as three operation zones-constant torque, maximum direct current, and maximum voltage—based on the driving scenario. The AFPM motor is designed to minimize energy consumption based on the motor weight and the frequent operating points of a driving cycle. The final result shows that the electric vehicle driven by the proposed AFPM motor consumes about 15% less energy than motors designed using traditional methods. Full article
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<p>(<b>a</b>) A schematic view of a four-wheel-drive electric passenger car; and (<b>b</b>) the half vehicle model.</p>
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<p>Torque and speed (TN) curve requirement for one motor.</p>
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<p>Dimensionless TN curves without current limit.</p>
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<p>Target TN curve, its corresponding DC and phase currents, and two boundaries of the TN curves.</p>
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<p>(<b>a</b>) Geometry of stator and rotor; (<b>b</b>) geometry of quasi-3-dimensional (quasi-3D) magnetic circuit model; and (<b>c</b>) 2-dimensional (2D) cross-sectional view.</p>
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<p>(<b>a</b>) Comparison of the air-gap flux density distributions between the analytical and 2D finite element (FE) methods (<span class="html-italic">s</span> = 0°E); and (<b>b</b>) comparison of the flux linkage of phase A between the analytical and 2D FE methods.</p>
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<p>(<b>a</b>) Flux density distribution from the 2D FE analysis; and (<b>b</b>) the dimensionless fringing-effect coefficient along the radial coordinate for various (<span class="html-italic">R</span><sub>o</sub>–<span class="html-italic">R</span><sub>i</sub>).</p>
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<p>(<b>a</b>) The target driving cycles; (<b>b</b>) the power loss; and (<b>c</b>) the distribution of the corresponding operating points on the TN map.</p>
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<p>(<b>a</b>) Exploded view of the axial-flux permanent-magnet (AFPM) motor assembly and the S-shaped water-cooling duct on housing; magnetic flux density distribution in (<b>b</b>) the stator, the rotor; and (<b>c</b>) the air gap at various radii.</p>
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<p>(<b>a</b>) Torque <span class="html-italic">versus</span> phase current curves from the ideal model of Equation (15) and the FE analysis; (<b>b</b>) <span class="html-italic">d</span>-axis flux and <span class="html-italic">q</span>-axis flux linkages <span class="html-italic">versus</span> the phase current; and (<b>c</b>) the resulting TN curve using the FE method as compared to the required TN curve and the TN curve obtained using the magnetic circuit model.</p>
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<p>(<b>a</b>) Efficiency map from the FE method; and (<b>b</b>) efficiency map difference between the FE and magnetic circuit models.</p>
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<p>(<b>a</b>) Temperature response of the AFPM motor at continuous operation; temperature distributions in (<b>b</b>) the stator; (<b>c</b>) the winding; and (<b>d</b>) the magnet at the steady-state.</p>
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<p>(<b>a</b>) Temperature response of the AFPM motor at continuous operation; temperature distributions in (<b>b</b>) the stator; (<b>c</b>) the winding; and (<b>d</b>) the magnet at the steady-state.</p>
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2430 KiB  
Article
The Effect of Biomass Physical Properties on Top-Lit Updraft Gasification of Woodchips
by Arthur M. James R., Wenqiao Yuan and Michael D. Boyette
Energies 2016, 9(4), 283; https://doi.org/10.3390/en9040283 - 12 Apr 2016
Cited by 42 | Viewed by 7605
Abstract
The performance of a top-lit updraft gasifier affected by biomass (pine wood) particle size, moisture content and compactness was studied in terms of the biochar yield, biomass burning rate, syngas composition and tar content. The highest biochar yield increase (from 12.2% to 21.8%) [...] Read more.
The performance of a top-lit updraft gasifier affected by biomass (pine wood) particle size, moisture content and compactness was studied in terms of the biochar yield, biomass burning rate, syngas composition and tar content. The highest biochar yield increase (from 12.2% to 21.8%) was achieved by varying the particle size from 7 to 30 mm, however, larger particles triggered tar generation that reached its maximum of 93.5 g/m3 syngas at 30-mm biomass particles; in contrast, the hydrogen content in syngas was at its minimum of 2.89% at this condition. The increase in moisture content from 10% to 22% reduced biochar yield from 12% to 9.9%. It also reduced the tar content from 12.9 to 6.2 g/m3 which was found to be the lowest range of tar content in this work. Similarly, the carbon monoxide composition in syngas decreased to its minimum of 11.16% at moisture content of 22%. Finally, the biomass compactness increased biochar yield up to 17% when the packing mass was 3 kg. However, the addition of compactness also increased the tar content in syngas, but little effect was noticed in syngas composition. Full article
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<p>The top-lit updraft biomass gasification system setup.</p>
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<p>(<b>a</b>) The biochar yield and (<b>b</b>) combustion zone temperature of wood chips gasification at varying particles sizes (airflow 20 lpm, moisture content 10%, and biomass compactness 0 kg). Different letters on data points indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) Gasification burning rate and (<b>b</b>) tar content in syngas at varying particle sizes (airflow rate 20 lpm, moisture content 10%, and biomass compactness 0 kg). Different letters on data points indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) Biochar yield and (<b>b</b>) combustion zone temperature of wood chips at different moisture contents (airflow 20 lpm, avg. particle size 7 mm, biomass compactness 0 kg). Different letters on data points indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) Burning rate and (<b>b</b>) tar content in syngas using biomass with varying moisture contents (airflow rate 20 lpm, avg. particle size 7 mm, biomass compactness 0 kg). Different letters on data points indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) Biochar yield and (<b>b</b>) combustion zone temperature using biomass with different biomass compactness. Airflow 20 lpm, 10% moisture content, avg. particle size 7 mm. Different letters on data points indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>(<b>a</b>) Burning rate and (<b>b</b>) tar content in syngas using biomass at different biomass compactness. Airflow 20 lpm, 10% moisture content, avg. particle size 7 mm. Different letters on data points indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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2170 KiB  
Article
Study on the Performance of the “Pendulor” Wave Energy Converter in an Array Configuration
by Sudath Prasanna Gunawardane, Chathura Jayan Kankanamge and Tomiji Watabe
Energies 2016, 9(4), 282; https://doi.org/10.3390/en9040282 - 12 Apr 2016
Cited by 14 | Viewed by 9045
Abstract
For over three decades the “Pendulor” wave energy device has had a significant influence in this field, triggering several research endeavours. It includes a top-hinged flap propelled by the standing waves produced in a caisson with a back wall on the leeward side. [...] Read more.
For over three decades the “Pendulor” wave energy device has had a significant influence in this field, triggering several research endeavours. It includes a top-hinged flap propelled by the standing waves produced in a caisson with a back wall on the leeward side. However, one of the main disadvantages which impedes its progress is the enormous expense involved in the construction of the custom made typical caisson structure, about a little more than one-quarter of the wave length. In this study, the influence of such design parameters on the performance of the device is investigated, via numerical modelling for a device arranged in an array configuration, for irregular waves. The potential wave theory is applied to derive the frequency-dependent hydrodynamic parameters by making a distinction in the fluid domain into a separate sea side and lee side. The Cummins equation was utilised for the development of the time domain equation of motion while the transfer function estimation methods were used to solve the convolution integrals. Finally, the device was tested numerically for irregular wave conditions for a 50 kW class unit. It was observed that in irregular wave operating conditions, the caisson chamber length could be reduced by 40% of the value estimated for the regular waves. Besides, the device demonstrated around 80% capture efficiency for irregular waves thus allowing provision for avoiding the employment of any active control. Full article
(This article belongs to the Special Issue Numerical Modelling of Wave and Tidal Energy)
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Graphical abstract

Graphical abstract
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<p>Concept of the “Pendulor” device [<a href="#B7-energies-09-00282" class="html-bibr">7</a>].</p>
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<p>Schematic representation of the Pendulor device in a breakwater ((<b>a</b>) side elevation; (<b>b</b>) plan elevation).</p>
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<p>Variation of added inertia (dotted line) and the radiation damping (solid line) with frequency <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>ω</mi> <mo>)</mo> </mrow> </semantics> </math>.</p>
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<p>Variation of added inertia (<math display="inline"> <semantics> <msub> <mi>I</mi> <mi>c</mi> </msub> </semantics> </math>) with wave frequency (<span class="html-italic">ω</span>).</p>
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<p>Impulse response of added inertia and radiation damping.</p>
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<p>(<b>a</b>) Added mass <math display="inline"> <semantics> <mrow> <mo>(</mo> <msub> <mi>I</mi> <mi>s</mi> </msub> <mo>)</mo> </mrow> </semantics> </math> <span class="html-italic">vs. ω</span>; (<b>b</b>) Radiation damping <math display="inline"> <semantics> <mrow> <mo>(</mo> <msub> <mi>B</mi> <mi>s</mi> </msub> <mo>)</mo> </mrow> </semantics> </math> <span class="html-italic">vs. ω</span>, indicates frequency domain data and transfer function approximation data (dashed red line; frequency domain data, solid line; transfer function approximation data).</p>
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<p>Added inertia <math display="inline"> <semantics> <mrow> <mo>(</mo> <msub> <mi>I</mi> <mi>c</mi> </msub> <mo>)</mo> </mrow> </semantics> </math> <span class="html-italic">vs. ω</span> of the chamber side for frequency domain and transfer function approximation (dashed red line; frequency domain data, solid line; transfer function approximation data).</p>
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<p>Amplitude of oscillations of the flap with time for different wave frequencies (Solution of Equation (<a href="#FD4-energies-09-00282" class="html-disp-formula">4</a>), dotted line; Solution of Equation (<a href="#FD25-energies-09-00282" class="html-disp-formula">25</a>), solid line).</p>
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<p>Dimensionless frequency <span class="html-italic">vs.</span> dimensionless chamber length. Solid line, <math display="inline"> <semantics> <msub> <mi>ω</mi> <mrow> <mi>n</mi> <mi>d</mi> </mrow> </msub> </semantics> </math>; vertical dashed lines, <math display="inline"> <semantics> <mrow> <msub> <mi>λ</mi> <mrow> <mi>n</mi> <mi>d</mi> </mrow> </msub> <mo>/</mo> <mn>4</mn> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <mn>3</mn> <msub> <mi>λ</mi> <mrow> <mi>n</mi> <mi>d</mi> </mrow> </msub> <mo>/</mo> <mn>4</mn> </mrow> </semantics> </math>.</p>
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<p>Variation of capture factor (<span class="html-italic">C<sub>f</sub></span>) with wave period (<span class="html-italic">T</span>), for variable (<b>a</b>) <span class="html-italic">d</span>, (<b>b</b>) <span class="html-italic">l</span>, (<b>c</b>) <span class="html-italic">h</span>, and (<b>d</b>) experimental [<a href="#B31-energies-09-00282" class="html-bibr">31</a>] and theoretical comparison of <span class="html-italic">C<sub>f</sub></span> with normalized wave length (<span class="html-italic">λ</span>/<span class="html-italic">d</span>).</p>
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<p>(<b>a</b>) Variation of capture factor (<span class="html-italic">C<sub>f</sub></span>) and (<b>b</b>) Amplitude of angle of oscillations (<span class="html-italic">θ</span><sub>0</sub>), with wave period (<span class="html-italic">T</span>), for different PTO system damping (<span class="html-italic">N</span>).</p>
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<p><math display="inline"> <semantics> <msub> <mi>C</mi> <mi>f</mi> </msub> </semantics> </math> variation between the linear (viscous) and coulomb damping models (blue line: <math display="inline"> <semantics> <msub> <mi>C</mi> <mi>f</mi> </msub> </semantics> </math> for linear (viscous) damping; black line: <math display="inline"> <semantics> <msub> <mi>C</mi> <mi>f</mi> </msub> </semantics> </math> for Coulomb damping).</p>
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<p>(<b>a</b>) Phase difference of <span class="html-italic">f<sub>Ex</sub> vs.</span> <math display="inline"> <semantics> <mover accent="true"> <mi>θ</mi> <mo>˙</mo> </mover> </semantics> </math> and (<b>b</b>) Cumulative energy capture for different loads with time.</p>
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<p>Spectral performance of the plant (solid lines indicate spectral <math display="inline"> <semantics> <msub> <mi>C</mi> <mi>f</mi> </msub> </semantics> </math> and the dashed lines show the <math display="inline"> <semantics> <msub> <mi>C</mi> <mi>f</mi> </msub> </semantics> </math> of a regular wave).</p>
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<p>Variation of <span class="html-italic">C<sub>f</sub></span> with (<b>a</b>) <span class="html-italic">T<sub>e</sub></span>; (<b>b</b>) <span class="html-italic">H<sub>s</sub></span> for different loading conditions; and (<b>c</b>) Optimum loading conditions for maximum <span class="html-italic">C<sub>f</sub></span> for different Hs.</p>
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<p>(<b>a</b>) Variation of angular velocity of the flap and wave excitation moment for different loading conditions; (<b>b</b>) Cumulative energy capture with time.</p>
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<p><math display="inline"> <semantics> <msub> <mi>C</mi> <mi>f</mi> </msub> </semantics> </math> for different chamber lengths <math display="inline"> <semantics> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> </semantics> </math> in the optimal control condition with <math display="inline"> <semantics> <msub> <mi>T</mi> <mi>e</mi> </msub> </semantics> </math> (s).</p>
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<p>(<b>a</b>) <span class="html-italic">C<sub>f</sub></span> variation with <span class="html-italic">T<sub>e</sub></span> s for varies values of <span class="html-italic">d</span> s without any controller; (<b>b</b>) Cumulative energy capture at <span class="html-italic">T<sub>e</sub></span> = 12 s by varying the chamber lengths.</p>
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<p>Graphical interpretation of superposition of piston type wave maker and bottom hinged flap wave maker.</p>
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1196 KiB  
Article
Vibration Durability Testing of Nickel Cobalt Aluminum Oxide (NCA) Lithium-Ion 18650 Battery Cells
by James Michael Hooper, James Marco, Gael Henri Chouchelamane, Christopher Lyness and James Taylor
Energies 2016, 9(4), 281; https://doi.org/10.3390/en9040281 - 12 Apr 2016
Cited by 27 | Viewed by 8548
Abstract
This paper outlines a study undertaken to determine if the electrical performance of Nickel Cobalt Aluminum Oxide (NCA) 3.1 Ah 18650 battery cells can be degraded by road induced vibration typical of an electric vehicle (EV) application. This study investigates if a particular [...] Read more.
This paper outlines a study undertaken to determine if the electrical performance of Nickel Cobalt Aluminum Oxide (NCA) 3.1 Ah 18650 battery cells can be degraded by road induced vibration typical of an electric vehicle (EV) application. This study investigates if a particular cell orientation within the battery assembly can result in different levels of cell degradation. The 18650 cells were evaluated in accordance with Society of Automotive Engineers (SAE) J2380 standard. This vibration test is synthesized to represent 100,000 miles of North American customer operation at the 90th percentile. This study identified that both the electrical performance and the mechanical properties of the NCA lithium-ion cells were relatively unaffected when exposed to vibration energy that is commensurate with a typical vehicle life. Minor changes observed in the cell’s electrical characteristics were deemed not to be statistically significant and more likely attributable to laboratory conditions during cell testing and storage. The same conclusion was found, irrespective of cell orientation during the test. Full article
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<p>Schematic of test process.</p>
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<p>Society of Automotive Engineers (SAE) J2380 vibration power spectral density (PSD) profiles for Testing Samples 1 to 9 [<a href="#B27-energies-09-00281" class="html-bibr">27</a>,<a href="#B32-energies-09-00281" class="html-bibr">32</a>].</p>
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<p>(<b>a</b>) Axis convention of vehicle vibration durability profiles; (<b>b</b>) Axis convention of cells [<a href="#B4-energies-09-00281" class="html-bibr">4</a>].</p>
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3141 KiB  
Article
Simultaneous Fault Detection and Sensor Selection for Condition Monitoring of Wind Turbines
by Wenna Zhang and Xiandong Ma
Energies 2016, 9(4), 280; https://doi.org/10.3390/en9040280 - 12 Apr 2016
Cited by 21 | Viewed by 5912
Abstract
Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault [...] Read more.
Data collected from the supervisory control and data acquisition (SCADA) system are used widely in wind farms to obtain operation and performance information about wind turbines. The paper presents a three-way model by means of parallel factor analysis (PARAFAC) for wind turbine fault detection and sensor selection, and evaluates the method with SCADA data obtained from an operational farm. The main characteristic of this new approach is that it can be used to simultaneously explore measurement sample profiles and sensors profiles to avoid discarding potentially relevant information for feature extraction. With K-means clustering method, the measurement data indicating normal, fault and alarm conditions of the wind turbines can be identified, and the sensor array can be optimised for effective condition monitoring. Full article
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<p>Active power of the turbine after removal of gaps.</p>
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<p>Scatter plots of the processed SCADA data: (<b>a</b>) Active power against wind speed; (<b>b</b>) Generator winding temperature against wind speed; (<b>c</b>) Active power output of a fault-free wind turbine.</p>
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<p>Graphical description of the PARAFAC model of <b><span class="underline">X</span></b> (samples × times × sensors).</p>
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<p>Loadings plot of the PARAFAC model for the sample mode (<b>a</b>1 <span class="html-italic">versus</span> <b>a</b>2), <b>a</b>1 and <b>a</b>2 are the first two columns of the loading matrix <b>A.</b></p>
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<p><span class="html-italic">K</span>-means clustering result of the loading matrix <b>A</b>, <b>a</b>1 and <b>a</b>2 are the first two columns of <b>A</b>.</p>
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<p>Active power output for fault detection: (<b>a</b>) Active power output against measurement time; (<b>b</b>) Active power output against wind speed.</p>
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<p>Generator winding temperature for fault detection: (<b>a</b>) Generator winding temperature against measurement time; (<b>b</b>) Generator winding temperature against wind speed.</p>
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<p>Loadings plot of the PARAFAC model for the sensor mode (<b>c</b>1 <span class="html-italic">versus</span>. <b>c</b>2): (<b>a</b>) Loadings plot of the PARAFAC model for sensor mode; (<b>b</b>) An enlarged view of the central cluster of (<b>a</b>) , <b>c</b>1 and <b>c</b>2 are the first two columns of the loading matrix <b>C</b>.</p>
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<p>Gearbox oil sump temperature for fault detection.</p>
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<p>Active power output produced by PARAFAC model from samples using an optimised sensor array: (<b>a</b>) Using sensors 1, 2, 9, 12, 14, 26, 33, 52; (<b>b</b>) Using sensors 1, 8, 11, 13, 14, 19, 29, 48.</p>
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<p>Active power output produced by PARAFAC model from samples using an optimised sensor array: (<b>a</b>) Using sensors 1, 2, 9, 12, 14, 26, 33, 52; (<b>b</b>) Using sensors 1, 8, 11, 13, 14, 19, 29, 48.</p>
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9540 KiB  
Article
Effect of Guide Vane Clearance Gap on Francis Turbine Performance
by Ravi Koirala, Baoshan Zhu and Hari Prasad Neopane
Energies 2016, 9(4), 275; https://doi.org/10.3390/en9040275 - 11 Apr 2016
Cited by 37 | Viewed by 11262
Abstract
Francis turbine guide vanes have pivoted support with external control mechanism, for conversion of pressure to kinetic energy and to direct them to runner vanes. This movement along the support is dependent on variation of load and flow (operating conditions). Small clearance gaps [...] Read more.
Francis turbine guide vanes have pivoted support with external control mechanism, for conversion of pressure to kinetic energy and to direct them to runner vanes. This movement along the support is dependent on variation of load and flow (operating conditions). Small clearance gaps between facing plates and the upper and lower guide vane tips are available to aid this movement, through which leakage flow occurs. This secondary flow disturbs the main flow stream, resulting performance loss. Additionally, these increased horseshoe vortex, in presence of sand, when crosses through the gaps, both the surfaces are eroded. This causes further serious effect on performance and structural property by increasing gaps. This paper discusses the observation of the severity in hydropower plants and effect of clearance gaps on general performance of the Francis turbine through computational methods. It also relates the primary result with the empirical relation for leakage flow prediction. Additionally, a possible method to computationally estimate thickness depletion has also been presented. With increasing clearance gap, leakage increases, which lowers energy conversion and turbine efficiency along with larger secondary vortex. Full article
(This article belongs to the Special Issue Selected Papers from 5th Asia-Pacific Forum on Renewable Energy)
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<p>Clearance gap in a Francis Turbine [<a href="#B6-energies-09-00275" class="html-bibr">6</a>].</p>
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<p>Guide Vane Cascade. 1: Outer Point in guide vane; 2: Inner Point in guide vane; R1: Radius of point 1; R2: Radius of point 2; v: Flow velocity in cascade.</p>
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<p>Losses in Francis turbine.</p>
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<p>Increased Clearance gaps at Kaligandaki A (KG-A) (<b>i</b>–<b>iii</b>); Erosion induced surface roughness at SKHP (<b>iv</b>); Facing plate erosion faces erosion at Bhilangana Hydropower Project (BHPP) (<b>v</b>,<b>vi</b>).</p>
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<p>Hexahedral mesh of guide vane made in ICEM and Runner made in Turbogrid. ICEM: powerful meshing software.</p>
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<p>Computational domain.</p>
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<p>Thickness depletion in the facing plate.</p>
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<p>Comparison between Empirical and Computational result. ΔC: Clearance gap, Q<sub>L</sub>: Leakage Flow. LPS: Liter Per Second, CFD: Computational Fluid Dynamics.</p>
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<p>Variation in hydraulic performance with clearance gap. Δ: Percentage change in values.</p>
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<p>Guide vane measurement position.</p>
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<p>Velocity profile over the gap from button to top at leading edge. V: Velocity profile.</p>
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<p>Velocity Profile over the gap from button to top at Trailing edge.</p>
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<p>Surface plots at the Leading Edge, Trailing edge and Radial view in the gap.</p>
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