The field of research in maximum power point tracking (MPPT) methods is experiencing great progress with a wide range of techniques being suggested, ranging from simple but ineffective methods to more effective but complex ones. Therefore, it is very important to propose a strategy that is both simple and effective in controlling the global maximum power point (GMPP) for a photovoltaic (PV) system under changing weather conditions, especially in partial shading cases (PSCs). This paper proposes a new design of an MPPT controller based on a metaheuristic optimization technique called Crow Search Algorithm (CSA) to attenuate the undesirable effects of partial shading on the tracking performances of standalone PV systems. CSA is a nature-inspired method based on the intelligent skills of the crow in the search process of hidden food places. CSA technique combines efficiency and simplicity using only two tuning parameters. The stability analysis of the proposed system is performed using a Lyapunov function. The simulation results for three different partial shading cases that are zero, weak and severe shading confirm the superior performance of CSA compared to PSO and P&O techniques in term of easy implementation, high efficiency and low power loss, decreasing considerably the convergence time by an average of 38.53%.
This paper presents the optimal operation of a photovoltaic pumping system. The operation of a permanent magnet brushless DC motor driving a centrifugal pump is investigated. The motor is controlled through a hysteresis current loop and an outer speed loop with a PI type controller. The proportional and integral gains are set to their optimal values. In order to optimise the overall system efficiency, a Maximum power point tracker is also used. Simulation is carried out by formulating the mathematical model for the photovoltaic source, MPPT, motor and pump load. System performances are investigated under different levels of solar insolation. The effectiveness of the proposed controller is also demonstrated.
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