An Intelligent Differential Evolution based Maximum Power Point Tracking (MPPT) Technique for Partially Shaded Photo Voltaic (PV) Array

The main aim of this study is to amend the maximum power point tracking (MPPT) for the photo voltaic (PV) array when it suffers from partially shaded conditions. When a photo voltaic panel is shaded for a fraction of time, the power output reduces invariably. During partially shaded conditions the power-voltage curves exhibit multiple maxima which makes the conventional MPPT techniques (perturb and observe, incremental conductance etc.) to get trapped in the local maxima. This paper proposes a Differential Evolution (DE) curdled algorithm to track the global maximum power point thereby increasing the performance of PV array and acquiring a better maxima point. The proposed algorithm is realized in MATLAB/Simulink environment. The credibility of the algorithm is ensured by comparing the results of DE algorithm with its well entrenched counterparts Particle Swarm Optimisation (PSO) MPPT technique and Ant Colony Algorithm (ACO). Also this study proves that the suggested technique would prevail over the most prominent Perturb and Observe (P&O) MPPT when the PV array is under shading conditions.

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