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Evolutionary algorithms (EAs) have emerged as powerful maximum power point trackers (MPPTs) for solar photovoltaic (PV) system under partial shading condition (PSC). However, due to stochastic nature of an EA, tracking performance is not similar during its each independent execution. Consequently, the effectiveness of such method cannot be benchmarked merely with single run or through average statistical results. This paper therefore presents a two-fold contribution. The first contribution demonstrates that run length distribution (RLD) is the most suitable methodology to evaluate the explicit success of EA inspired MPPTs during each independent run. For test case, particle swarm optimization (PSO) is taken as an MPPT algorithm. Extensive simulations and experimentations of six PSO variants are carried out against three partially shaded power-voltage (P-V) curves, including the complex and experimental curve. Each algorithm is run 50 times and results are evaluated through RLD test, which is never used in most of the research for MPPT rationale. The second contribution involves the development of a generic adaptive PSO (APSO), which clearly highlights the importance of parameters’ improvisation of PSO as an MPPT algorithm. The designed APSO obtains almost 100% success rate with minimum evaluation count to converge at global peak (GP) under PSC.
Javed et al. (Mon,) studied this question.