Search Results (328)

The efficiency of standalone photovoltaic (PV) systems heavily relies on the effectiveness of their maximum power point tracking (MPPT) controller. This study aims to improve the operational efficiency and reliability of standalone PV systems by introducing a novel control scheme, the Immersion and Invariance Neural Network (II-NN). This innovative system integrates a nonlinear estimator of solar irradiance with a neural network (NN) model, eliminating the need for direct irradiance measurements and associated costly sensors. The proposed methodology uses the Immersion and Invariance algorithm to design a nonlinear estimator that leverages the real-time measurements of PV current and voltage to estimate the incident irradiance. The NN then processes this estimated irradiance to determine the MPP voltage accurately. A robust nonlinear controller ensures the PV system operates at the MPP. This approach stands out by managing the nonlinearities, parametric uncertainties, and dynamic variations in PV systems without relying on direct irradiance measurements. The II-NN system was rigorously tested and validated under real climatic conditions, providing a realistic performance assessment. The principal results show that the II-NN system achieves a mean error of 0.0183V and a mean absolute percentage error of 0.3913%, with an overall MPPT efficiency of up to 99.84%. Comparisons with the existing methods, including perturb and observe, incremental conductance, and three other recent algorithms, reveal that the II-NN system outperforms these alternatives. The major conclusion is that the II-NN algorithm significantly enhances the operational efficiency of PV systems while simplifying their implementation, making them more cost-effective and accessible. This study substantially contributes to PV system control by advancing a robust, intelligent, and sensorless MPPT control scheme that maintains high performance even under varying and unpredictable climatic conditions. Full article

The issue of energy supply for wireless sensors is becoming increasingly severe with the advancement of the Fourth Industrial Revolution. Thus, this paper proposed a thermoelectric self−powered wireless sensor that can harvest industrial waste heat for self−powered operations. The results show that this self−powered wireless sensor can operate stably under the data transmission cycle of 39.38 s when the heat source temperature is 70 °C. Only 19.57% of electricity generated by a thermoelectric power generation system (TPGS) is available for use. Before this, the power consumption of this wireless sensor had been accurately measured, which is 326 mW in 0.08 s active mode and 5.45 μW in dormant mode. Then, the verified simulation model was established and used to investigate the generation performance of the TPGS under the Dirichlet, Neumann, and Robin boundary conditions. The minimum demand for a heat source is cleared for various data transmission cycles of wireless sensors. Low−temperature industrial waste heat is enough to drive the wireless sensor with a data transmission cycle of 30 s. Subsequently, the economic benefit of the thermoelectric self−powered system was also analyzed. The cost of one thermoelectric self−powered system is EUR 9.1, only 42% of the high−performance battery cost. Finally, the SEPIC converter model was established to conduct MPPT optimization for the TEG module and the output power can increase by up to approximately 47%. This thermoelectric self−powered wireless sensor can accelerate the process of achieving energy independence for wireless sensors and promote the Fourth Industrial Revolution. Full article

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers. Full article

When the battery or supercapacitor is connected to the output of a PV system, the conventional voltage equation expressing its mathematical model usually must be replaced by the current relationship to study the maximum power point tracking (MPPT) control theory. However, hitherto, there is a lack of an attempt to disclose the current constraint relationships at the maximum power point (MPP), which leads to the potential risk of MPPT failure. To solve this problem, in this paper, the MPPT constraint conditions on the basis of currents are built and then a new MPPT control strategy is proposed. In this strategy, a linearized model parameter of a PV cell is used as the bridge to find the current relationships. On the basis of them, some expressions involving the duty cycle are built to directly calculate the control signal of the MPPT controller. Meanwhile, an implementation method is designed to match this proposed MPPT strategy. Finally, some simulation experiments are conducted. The simulation results verify that the proposed MPPT constraint expressions are accurate and workable and that the proposed MPPT strategy and its implementation process are feasible and available. In addition, the simulation results also show that the proposed MPPT strategy has a better MPPT speed and the same MPPT accuracy when the P&O method and fuzzy algorithm are compared. By this work, the MPPT constraint conditions based on current relationships are first found, representing a breakthrough in disclosing the inherent relationships between different currents when the PV system is operating around the MPP. Full article

This study presents a new approach for Maximum Power Point Tracking (MPPT) by combining the honey badger algorithm (HBA) with a Genetic Algorithm (GA). The integration aims to optimize photovoltaic (PV) system performance in partial shading conditions (PSCs). Initially, the HBA is utilized to explore extensively and identify potential solutions while avoiding local optima. If necessary, the GA is then employed to escape local optima through selection, crossover, and mutation operations. On average, this proposed method has a 40% improvement in tracking time and 0.77% in efficiency compared with the HBA. In a dynamic case, the proposed method achieves a 4.81% improvement compared to HBA. Full article

The primary objective of this paper focuses on developing a control approach to improve the operational performance of a three-level neutral point clamped (3LNPC) shunt active power filter (SAPF) within a grid-tied PV system configuration. Indeed, this developed control approach, based on the used 3LNPC-SAPF topology, aims to ensure the seamless integration of a photovoltaic system into the three-phase four-wire grid while effectively mitigating grid harmonics, grid current unbalance, ensuring grid unit power factor by compensating the load reactive power, and allowing power sharing with the grid in case of an excess of generated power from the PV system, leading to overall high power quality at the grid side. This developed approach is based initially on the application of the four-wire instantaneous p-q theory for the identification of the reference currents that have to be injected by the 3LNPC-SAPF in the grid point of common coupling (PCC). Whereas, the 3LNPC is controlled based on using the finite control set model predictive control (FCS-MPC), which can be accomplished by determining the convenient set of switch states leading to the voltage vector, which is the most suitable to ensure the minimization of the selected cost function. Furthermore, the used topology requires a constant DC-link voltage and balanced split-capacitor voltages at the input side of the 3LNPN. Hence, the cost function is adjusted by the addition of another term with a selected weighting factor related to these voltages to ensure their precise control following the required reference values. However, due to the random changes in solar irradiance and, furthermore, to ensure efficient operation of the proposed topology, the PV system is connected to the 3LNPN-SAPF via a DC/DC boost converter to ensure the stability of the 3LNPN input voltage within the reference value, which is achieved in this paper based on the use of the maximum power point tracking (MPPT) technique. For the validation of the proposed control technique and the functionality of the used topology, a set of simulations has been presented and investigated in this paper following different irradiance profile scenarios such as a constant irradiance profile and a variables irradiance profile where the main aim is to prove the effectiveness and flexibility of the proposed approach under variable irradiance conditions. The obtained results based on the simulations carried out in this study demonstrate that the proposed control approach with the used topology under different loads such as linear, non-linear, and unbalanced can effectively reduce the harmonics, eliminating the unbalance in the currents and compensating for the reactive component contained in the grid side. The obtained results prove also that the proposed control ensures a consistent flow of power based on the sharing principle between the grid and the PV system as well as enabling the efficient satisfaction of the load demand. It can be said that the proposal presented in this paper has been proven to have many dominant features such as the ability to accurately estimate the power sharing between the grid and the PV system for ensuring the harmonics elimination, the reactive power compensation, and the elimination of the neutral current based on the zero-sequence component compensation, even under variable irradiance conditions. This feature makes the used topology and the developed control a valuable tool for power quality improvement and grid stability enhancement with low cost and under clean energy. Full article

The primary disadvantage of solar photovoltaic systems, particularly in partial shadowing conditions (PSC), is their low efficiency. A power–voltage curve with a homogenous distribution of solar irradiation often has a single maximum power point (MPP). Without a doubt, it can be extracted using any conventional tracker—for instance, perturb and observe. On the other hand, under PSC, the situation is entirely different since, depending on the number of distinct solar irradiation levels, the power–voltage curve has numerous MPPs (i.e., multiple local points and one global point). Conventional MPPTs can only extract the first point since they are unable to distinguish between local and global MPP. Thus, to track the global MPP, an optimized MPPT based on optimization algorithms is needed. The majority of global MPPT techniques seen in the literature call for sensors for voltage and current in addition to, occasionally, temperature and/or solar irradiance, which raises the cost of the system. Therefore, a single-sensor global MPPT based on the recent red-tailed hawk (RTH) algorithm for a PV system interconnected with a DC link operating under PSC is presented. Reducing the number of sensors leads to a decrease in the cost of a controller. To prove the superiority of the RTH, the results are compared with several metaheuristic algorithms. Three shading scenarios are considered, with the idea of changing the shading scenario to change the location of the global MPP to measure the consistency of the algorithms. The results verified the effectiveness of the suggested global MPPT based on the RTH in precisely capturing the global MPP compared with other methods. As an example, for the first shading situation, the mean PV power values varied between 6835.63 W and 5925.58 W. The RTH reaches the highest PV power of 6835.63 W flowing through particle swarm optimization (6808.64 W), whereas greylag goose optimizer achieved the smallest PV power production of 5925.58 W. Full article

This paper proposes a Symmetric High Voltage-Gain (SHVG) boost converter control for photovoltaic system applications. The concept is based on a multilevel boost converter configuration, which presents an advantage compared to a classic boost converter such as the ability to transfer a high amount of power with less stress on the power electronics components in the high voltage-gain conditions. This advantage allows the power losses in the converter to be reduced. A mathematical-based voltage model of the PV system using variable series resistance depending on solar irradiance and the temperature is proposed. This model is connected to an SHVG boost converter to supply the load’s power. A control strategy of the DC-bus voltage with maximum power point tracking (MPPT) from the PV system using PI controllers is developed. The contributions of the paper are focused on the SHVG operating analysis with the passive components’ sizing, and the DC-bus voltage control with maximum power point tracking of the PV systems in dynamic operating conditions. The performances of the proposed control are evaluated through simulations, where the results are interesting for high-power photovoltaic applications. Full article

This study presents an enhanced, adaptive, and dynamic surface sliding mode control (SMC), a cutting-edge method for improving grid-connected photovoltaic (PV) system performance. The suggested control approach uses dynamic SMC and adaptive approaches to enhance the robustness and efficiency of a system. Proportional–integral (PI) and SMC, two control systems for maximum power point tracking (MPPT) in PV systems, are compared in this paper. This study finds that the SMC system is a more effective and efficient MPPT approach for PV systems compared to the conventional PI control system. The SMC system’s unique feature is the capacity to stabilize grid voltage and attain a modulation index of less than one. An important component of power electronic system control is the index, which acts as a parameter representing the relationship between the output signal’s amplitude and the reference signal’s amplitude. The SMC method demonstrates improved robustness, efficiency, and stability, especially in dynamic operating settings with load and solar radiation changes. Compared to the PI control, the SMC exhibits a noteworthy 75% reduction in voltage fluctuations and an improvement in the power output of 5% to 10%. Regarding output power optimization, voltage stability, and accurate current tracking, the SMC system performs better than the PI control system. Furthermore, the SMC technique maintains a modulation index below one and guarantees grid voltage stability, both of which are essential for the efficiency and stability of power electrical systems. Full article

This paper analyzes dynamic modeling for rural HPS to address GHG emissions’ environmental impact on floods and climate change. The aim is to integrate renewable energy sources, such as solar energy, with traditional generators to mitigate emissions and enhance energy access in rural communities in Pakistan. The system is designed using a DC-DC converter, MPPT, LCL filter, and a DC-AC inverter. Utilizing software tools like PVsyst 7.4 and HOMER Pro-3.18.1, the study evaluates system sizing, energy consumption patterns, and optimization strategies tailored to site-specific data. The expected results include a reliable, environmentally friendly hybrid power system capable of providing consistent electricity to rural areas. The analysis of a connected load of 137.48 kWh/d and a peak load of 33.54 kW demonstrates the system’s promise for reliable electricity with minimal environmental impact. The estimated capital cost of USD 102,310 and energy generation at USD 0.158 per unit underscores economic feasibility. Dynamic modeling and validation using HIL examine the system’s behavior in response to variations in solar irradiance and temperature, offering insights into operational efficiency and reliability. The study concludes that the hybrid power system is scalable for rural energy access, which is a practical solution achieving a 100% renewable energy fraction, significantly contributing to emission reduction and promoting sustainable energy practices. Full article

Emerging magnetic lead screws (MLSs) have been proven to be promising in sustainable wave energy conversion areas due to their high efficiency and power density. This study is aimed at developing a constrained maximum power point tracking (MPPT) strategy for MLS-based wave energy converters (WECs). In this paper, the mechanism of the MLS is analyzed and the dynamic model of the MLS-based WEC is established. The variations in hydrodynamic coefficients were analyzed using ANSYS AQWA, based on which the theoretical MPPT requirements were explored. Afterward, two constraints (stroke and translator force constraint) were introduced to ensure the safe operation of the converter. An adaptive constrained genetic algorithm (ACGA) was applied to realize MPPT under constraints. For irregular wave situations, an extended Kalman filter (EKF) was applied to estimate the frequency and amplitude of the wave excitation force with which the constrained GA can be realized. Simulations and experiments were carried out to verify the constrained MPPT. In the two cases (wind speed u = 7 m/s and u = 10 m/s) of the simulation, the proposed ACGA can improve the energy harvest rate by 3.95% and 3.57% compared to the standard constrained genetic algorithm (SCGA), while this rate was improved by 6% in the experimental case. Full article

Floating photovoltaic (FPV) power generation technology has gained widespread attention due to its advantages, which include the lack of the need to occupy land resources, low risk of power limitations, high power generation efficiency, reduced water evaporation, and the conservation of water resources. However, FPV systems also face challenges, such as a significant impact from aquatic environments on the system’s stability and safety and high operational and maintenance costs, leading to large fluctuations in the grid-connected power output. Therefore, it is necessary to integrate energy storage devices with FPV systems to form an integrated floating photovoltaic energy storage system that facilitates the secure supply of power. This study investigates the theoretical and practical issues of integrated floating photovoltaic energy storage systems. A novel integrated floating photovoltaic energy storage system was designed with a photovoltaic power generation capacity of 14 kW and an energy storage capacity of 18.8 kW/100 kWh. The control methods for photovoltaic cells and energy storage batteries were analyzed. The coordinated control of photovoltaic cells was achieved through MPPT control and improved droop control, while the coordinated control of energy storage batteries involved a droop charge–discharge mode, a constant-voltage charging mode, and a standby mode. The simulations were realized in MATLAB/Simulink and the results validated the effectiveness of the coordinated control strategy proposed in this study. The strategy achieved operational stability and efficiency of the integrated photovoltaic energy storage system. Full article

Due to its simplicity, the one-diode model is commonly used for modeling the operation of photovoltaic (PV) modules at standard test conditions (STC). However, its inherent implicit nature often presents challenges in modeling PV energy production. In this paper, the innovative explicit one-diode model developed by us over time is adapted for estimating PV power production under real weather conditions. Simple yet accurate equations for calculating the energy output of a PV generator equipped with a maximum power point tracking (MPPT) system are proposed. The model’s performance is assessed under various normal and harsh operating conditions against measured data collected from the experimental setup located at the Solar Platform at West University of Timisoara, Romania. As an application of the new equation for maximum power, this paper presents a case study where the energy loss in the absence of an MPPT system is evaluated based on atmospheric and sky conditions. Full article

Due to the nonlinear relation between the generated power and voltage of photovoltaic (PV) arrays, there is a need to stimulate PV arrays to operate at maximum possible power. Maximum power can be tracked using the maximum power point tracker (MPPT). Due to the presence of several peaks on the power–voltage (P–V) characteristics of the shaded PV array, conventional MPPT such as hill climbing may show premature convergence, which can significantly reduce the generated power. Metaheuristic optimization algorithms (MOAs) have been used to avoid this problem. The main shortcomings of MOAs are the low convergence speed and the high ripples in the waveforms. Several strategies have been introduced to shorten the convergence time (CT) and improve the accuracy of convergence. The proposed technique sequentially uses a recent optimization algorithm called Mexican Axolotl Optimization (MAO) to capture the vicinity of the global peak of the P–V characteristics and move the control to a fuzzy logic controller (FLC) to accurately track the maximum power point. The proposed strategy extracts both the benefits of the MAO and FLC and avoids their limitations with the use of the high exploration involved in the MOA at the beginning of optimization and uses the fine accuracy of the FLC to fine-track the MPP. The results obtained from the proposed strategy show a substantial reduction in the CT and the highest accuracy of the global peak, which easily proves its superiority compared to other MPPT algorithms. Full article

Static waste heat recovery, by means of thermoelectric generator (TEG) modules, constitutes a fast-growing energy harvesting technology on the way towards greener transportation. Many commercial solutions are already available for small internal combustion engine (ICE) vehicles, whereas further development and cost reductions of TEG devices expand their applicability at higher-power transportation means (i.e., ships and aircrafts). In this light, the integration of waste heat recovery based on TEG modules in a shipboard distribution network is studied in this work. Several voltage step-up techniques are considered, whereas the most suitable ones are assessed via the LTspice simulation platform. The design procedure of the selected LLC resonant converter is presented and analyzed in detail. Furthermore, a flexible control strategy is proposed, capable of either output voltage regulation (constant voltage) or maximum power point tracking (MPPT), according to the application demands. Finally, both simulations and experiments (on a suitable laboratory testbench) are performed. The obtained measurements indicate the high efficiency that can be achieved with the LLC converter for a wide operating area as well as the functionality and adequate performance of the control scheme in both operating conditions. Full article