Abstract
Given the rapidly changing operating conditions of photovoltaic (PV) systems, the maximum power point tracking (MPPT) controllers that respond quickly to adverse operating conditions are crucial. The design of those controllers is considered a complex task and several approaches have been proposed. In this paper different MPPT controllers via metaheuristic algorithm for PV systems are compared. With the purpose of evaluate the performance of MPPT controllers that use evolutionary, physics-based, swarm-based, and human-based metaheuristic algorithms to reach the global maximum power point (GMPP), a comparative study of eight MPPT metaheuristics was carried out under partial shading conditions (PSC), considering the number of evaluations, tracking time, efficiency and successful rate. The classical perturb and observe (P&O) method was also considered for comparative purposes. For the simulation analysis three case studies under partial shading with different complexity (multiple power peaks) were considered. Overall, the results showed that MPPT metaheuristics perform very competitively to find the GMPP. Moreover, considering the several indicators, evolutionary and swarm-based metaheuristic algorithms showed the best performance, namely particle swarm optimization (PSO).
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