A Hybrid Evolutionary-Based MPPT for Photovoltaic Systems under Partial Shading Conditions

Under partial shading conditions (PSCs), photovoltaic (PV) system characteristics vary and may have multiple power peaks. Conventional maximum power point tracking (MPPT) methods are unable to track the global peak. In addition, it takes a considerable time to reach the maximum power point (MPP). To address these issues, this paper proposes an improved hybrid MPPT method using the conventional evolutional algorithms, i.e., Particle Swarm Optimization (PSO) and Differential Evaluation (DE). The main feature of the proposed hybrid MPPT method is the advantage of one method compensates for shortcomings of the other method. Furthermore, the algorithm is simple and rapid. It can be easily implemented on a low-cost microcontroller. To evaluate the performance of the proposed method, MATLAB simulations are carried out under different PSCc. Experimental verifications are conducted using a boost converter setup, an ET-M53695 panel and a TMS320F28335 DSP. Finally, the simulation and hardware results are compared to those from the PSO and DE methods. The superiority of the hybrid method over PSO and DE methods is highlighted through the results. © 2013 IEEE.

differential evaluation; maximum power point tracking; partial shading condition; particle swarm optimization; Photovoltaic systems DC-DC converters; MATLAB; Maximum power point trackers; Photovoltaic cells; Differential evaluation; Evolutional algorithm; Experimental verification; Matlab simulations; Maximum power point; Maximum Power Point Tracking; Partial shading; Photovoltaic systems; Particle swarm optimization (PSO)

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