The characteristics of photovoltaic (PV) systems can vary, resulting in several power peaks, when partially shaded. Traditional methods, which are often used to track maximum power peak (MPP) at normal environmental conditions, are unable to detect global maximum power peak (GMPP) under partial shading condition (PSC). This paper develops a new metaheuristic optimization MPPT method to tackle this problem. The method was created by combining the best aspects of bat algorithm (BA) with particle swarm optimization (PSO). The advantages of one method remunerate for the drawbacks of the other method, in this case the proposed MPPT method has distinct advantages. In addition, the algorithm is simple and fast. PSIM simulations are undertaken under various PSC to assess the performance of the proposed method. Therefore, the results of the present method are compared through simulation with those obtained by the BA and PSO methods. The findings demonstrate how the proposed method outperforms both the BA method and the PSO method. Finally, this paper provides a comprehensive comparison of the proposed method to current soft computing methods from the literature review.
Hybrid intelligent systems, Maximum power point tracker, Particle swarm optimization, Photovoltaic systems, Solar power generation.
Muhannad ALSHAREEF, "A New Particle Swarm Optimization with Bat Algorithm Parameter-Based MPPT for Photovoltaic Systems under Partial Shading Conditions", Studies in Informatics and Control, ISSN 1220-1766, vol. 31(4), pp. 53-66, 2022. https://doi.org/10.24846/v31i4y202206
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