P&O

Several algorithms have been offered to track the Maximum Power Point when we have one maximum power point. Moreover, fuzzy control and neural was utilized to track the Maximum Power Point when we have multi-peaks power points. In this paper, we will propose an improved Maximum Power Point tracking method for the photovoltaic system utilizing a modified PSO algorithm. The main advantage of the method is the decreasing of the steady state oscillation (to practically zero) once the Maximum Power Point is located. moreover, the proposed method has the ability to track the Maximum Power Point for the extreme environmental condition that cause the presence of maximum multi-power points, for example, partial shading condition and large fluctuations of insolation. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging circumstance, namely step changes in irradiance, step changes in load, and partial shading of the Photovoltaic array. Finally, its performance is compared with the perturbation and observation" and fuzzy logic results for the single peak, and the neural-fuzzy control results for the multi-peaks.

This paper presents a grid connected multilevel topology for photovoltaic (PV) systems. Usually, multilevel converters for PV application suffer from a distorted output current and voltage when the submodules are not subjected to an even solar irradiance. The difference in submodules irradiance results in different submodules duty cycles when maintaining the maximum power point tracking (MPPT). The distortion of the output current is proportional with the difference of the cells duty cycles. To this regard, a multilevel topology for PV applications is proposed along with a control and modulation strategy. In this proposed topology, H6 bridge-based cell is used instead of an H-bridge one. In case of solar irradiance mismatch, the proposed converter injects power with less voltage from the shaded cells without altering the PV voltage, and hence, the MPPT. This modification allows retaining a tantamount duty cycle in all cells whatever the meteorological conditions are present. To test the effectiveness of the proposed idea, a detailed simulation model was set up. The results show that the proposed concept provides a significantly improved output current quality compared to the cascaded H-bridge topology.

C’est une ville portuaire d’alors 22.000 habitants, Port-Louis, ancienne possession française de l’Océan indien, qui sera ce mois-ci notre destination. La desserte postale de l’île Maurice illustre en effet à quel point la recherche de liaisons efficaces a pu entraîner parfois des modifications radicales dans le tracé des routes maritimes entre Europe et contrées d’outre-mer. Nous rappelant, encore aujourd’hui, à quel point histoire et géographie postales sont intimement liées.

When the GPV is under partial shading, several peaks appear in the characteristic P-V, namely a GMP and one or more local maximums. The classical algorithm 'P&O' MPPT cannot converge on the GMP for low irradiation values and is trapped by tracking down a LMP so making the algorithm ineffective making the algorithm ineffective, in this case under 200 W/m². An alternative objective function is developed to optimize the performance of the FLC by selecting the appropriate gains using PSO. In this simulation the GPV is composed of one hundred modules grouped parallel series (10x10) and subjected to partial shading. The proposed FLC provides better performance for GMP tracking for the chosen shade configuration selected.

Impedance network inverters are a good alternative for voltage-source and current-source inverters. The shoot-through solution and the boosting capability of such converters make them an excellent solution for photovoltaic (PV) application. Furthermore, energy storage integration in these inverters does not require any additional components in the converter; indeed, a battery can be directly connected in parallel with one of the capacitors of the Z-or quasi Z-network. However, for an optimal control of these converters, complex control and modulation strategies are required. Model Predictive Control (MPC) provides high control performance at the expense of the computational effort. In this paper, a low computational control method where both MPC and proportional resonant (PR) controller are combined, is proposed. This makes the proposed controller perform two iterations only instead of iterating for all the available switching states. As shown in the obtained results, the proposed controller conserves the high performance of the conventional MPC with 50% less computational burden.

Dès la fin des années 1840, Ceylan était devenue beaucoup plus qu’une simple escale sur la « Route des Indes » : l’île sous souveraineté britannique allait alors assumer le rôle de véritable plaque tournante du trafic des paquebots-poste de la puissante compagnie P&O.

In cascaded multilevel quasi Z-source inverters (CM-qZSI), the intermittent and stochastic fluctuation of the solar power injected to the grid can be smoothened by connecting a battery in parallel with one of the qZ network capacitors. However, since CM-qZSI is a special case of the single phase qZSI, it suffers from some of its demerits, such as double line-frequency ripple. This affects the battery current, the second qZ network's inductor current, and the qZ network's capacitor voltages. This issue can be alleviated either by increasing the size of the passive elements or by developing advanced control schemes, such as MPC. However, MPC is computationally intensive, especially in multilevel converters. In this paper, a low computational control method for PV-fed battery assisted CM-qZSI is proposed, where proportional-resonant (PR) controller is introduced in the control algorithm. The prediction is performed only for the dc side of the converter; hence, the algorithm iterates for the shoot through and zero states only. As shown in the obtained results, by using the proposed method, the double line-frequency ripple can be significantly reduced, while a less computational effort compared to the conventional MPC is needed.

— Renewables are the need of the day. In todays world when there in high increase in demand of electric power renewables are the saviors. Distributed Generators (DG) Ie. renewables are the power sources located near load centers which are provided with power electronics converters for grid interfacing. As the energy produced by renewbles vary maximum power point tracking is used to trace maximum power from the renewables. In this paper solar is used in choice of renewables and perturb and observe is used as the MPPT method to trace the maximum power point of solar photovoltic. The system consists of solar photovoltic model with a DC-DC converter in which MPPT method is implemented. A 700KW Grid integrated Photovoltaic system is simulated by MATLAB/SIMULINK software along with the proposed control strategy and the results show the feasibility and performance of the control structure.

Maximum power point tracking (MPPT) algorithms are employed in photovoltaic (PV) systems to make full utilization of PV array output power, which have a complex relationship between ambient temperature and solar irradiation. The power-voltage characteristic of PV array operating under partial shading conditions (PSC) exhibits multiple local maximum power points (LMPP). In this paper, an advanced algorithm has been presented to track the global maximum power point (GMPP) of PV. Compared with the Perturb and Observe (P&O) techniques, the algorithm proposed the advantages of determining the location of GMPP whether partial shading is present.

31 août 1859, la compagnie maritime britannique P&O inaugure sur sa ligne d’Extrême-Orient une nouvelle liaison au départ de la Chine vers le Japon, reliant ainsi Shanghai à Nagasaki. S’il est une destination politiquement sensible dans les années 1850, c’est bien celle-ci : l’acheminement du courrier entre l’Europe et le  pays du Soleil Levant est en effet à l’image des relations ambiguës qu’entretiennent alors ces deux mondes.

The applications of photovoltaic energy are steadily increasing. However, the output power of a photovoltaic system is influenced by the meteorological conditions (temperature and irradiation), which can affect the production and efficiency of the photovoltaic panel energy. So to ensure that the photovoltaic panel produces its maximum power possible at any time and regardless of the external conditions, we use the equipment called MPPT (maximum power point tracker). Sliding mode control is recognized by its stability and robustness and widely used in non-linear systems but represents a disadvantage because of the chattering phenomena. So in order to overcome this problem and improve this control, we opted to add to it the adaptive fuzzy control which will adapt the switching gain. This controller is named AFSMC (adaptive fuzzy sliding mode control) and will be compared the sliding mode controller SMC in order to see the difference and to the P&O (perturb & observe) method to validate this controller. Our system consists of a photovoltaic panel a DC-DC boost converter and a centrifugal pump with a DC motor. All simulations are carried out under MATLAB/SIMULINK and the extracted results show the efficiency of the AFSMC controller and confirm that the new methods have improved energy efficiency and production.

This  paper  presented  the  study,  development  and  implementation of  the maximum  power  point  of  a  photovoltaic  energy  generator  adapted by elevator converter and controlled by a maximum power point command. In order  to  improve  photovoltaic  system  performance  and  to  force  the photovoltaic generator to operate at its maximum power point, the idea of the context  of  this  paper  deals  with  the  exploitation  of  the  technique  of  the artificial  intelligence  mechanism  (neural  network)  certainly  based  on  the three  parts  of  the  photovoltaic  system  (photovoltaic  module  inputs (temperature and solar radiation), photovoltaic module and control (MPPT)) that have been adopted within a simulation time of 24 hours. In addition, to reach  the  optimal  operating  point  regardless  of  variations  in  climatic conditions, the use of a neuron network based disturbance and observation algorithm (P & O) is put into service of the system given its reliability, its simplicity  and  view  that  at  any  time  it  can  follow  the  desired maximum power.  The  entire  system  is  implemented  in  the  Matlab/Simulink environment where simulation results obtained are very promising and have shown the effectiveness and speed of neural technology that still require a learning base so to improve the performance of photovoltaic systems and exploit them in energy production, as well as this technique has proved that these results are much better in terms (of its very great precision and speed of computation) than those of the controller based on the conventional MPPT method P & O.

The power supplied by photovoltaic DC-DC converter is affected by two factors, sun irradiance and temperature. Therefore, to improve the performance of the PV system; a mechanism to track the maximum power point (MPP) is required. Conventional maximum power point tracking approaches, such as observation and perturbation technique present some difficulties in identifying the true MPP. Therefore, intelligent systems including fuzzy logic controllers (FLC) are introduced for the maximum power point tracking system (MPPT). In this paper, we present a comparative study of the PV standalone system which is controlled by three techniques. The first one is conventional based on the observation and perturbation technique, the other are intelligent based on fuzzy logic according Mamdani and Takagi-Sugeno models. The investigations show that the fuzzy logic controllers provide the best results and Takagi-Sugeno model presents the lower overshoot value.

In this paper, a comparison is carried out between MPPT technique using a hybrid cascaded Fuzzy-PI Controller and Perturb and Observe (P&O) MPPT technique. The two techniques are compared in a system which consists of a photovoltaic system connected to a grid via a two-level three-phase inverter. The P&O and Fuzzy-PI techniques are examined under different weather conditions, namely, ramp change in irradiation level and ramp change in temperature levels. Disturbances represented by fluctuations in power demand from the utility grid is generated to assess the robustness of each of the two controllers. The Fuzzy-PI based MPPT, has proved its excellent tracking and robustness against abrupt perturbations. Simulations of the system performances using the two regulators are performed to validate the conceptual standings.

This paper addresses a performance evaluation of maximum power point tracking techniques (MPPT) in a photovoltaic system. This research work finds its applications in photovoltaic systems producing electric power with a better energy efficiency, which will lead to an improved relationship between the cost and the amount of the produced power. The importance of this work resides on the one hand in the evaluation of the performances of the different MPPTs according to three criteria instead of one or two criteria in other works of the literature and on the other hand in the study of Four algorithms in one paper and their comparisons. This paper discusses the performance evaluation of the MPPT algorithms called P&O, Inc-Cond, Hill-Climbing and Fuzzy algorithms based simulation results and practical validation. The performances of these algorithms are evaluated according to the following criteria: The response time, the amplitude of the oscillations around the optimal point and the accuracy. The objectives in this article are summarized in the following points: (a) modeling the photovoltaic systems, (b) presenting and detailing each MPPT algorithm (c) pressenting and discussing the simulation results in Matlab/Simulink and practical validation (d) evaluating the performance of each algorithm. This paper is completed by a summary on the areas of use for each algorithm and conclusions.

The demand for renewable energy has increased because it is considered a clean energy and does not result in any pollution or emission of toxic gases that negatively affect the environment and human health also requiring little maintenance, and emitting no noise, so it is necessary to develop this type of energy and increase its production capacity. In this research a design of maximum power point tracking (MPPT) control method using Neural Network (NN) for photovoltaic system is presented. First we design a standalone PV system linked to dc boost chopper with MPPT by perturbation and observation P&O technique, and then a design of MPPT by using ANN for the same system is presented. Comparative between two control methods are studied. The results explained in constant and adjustable weather settings such as irradiation and temperature. The results exposed that the proposed MPPT by ANN control can improve the PV array efficiency by reduce the oscillation around the MPP that accure in P&O method and so decreases the power losses. As well as decrease the the overshot that accure in transient response, and hence improving the performance of the solar cell.