Optimal Selection of Evolutionary Based MPPT Algorithm for PV Arrays Under Multiple Partial Shading Conditions

Considering significant interest in clean energy in recent times, integration of photovoltaic (PV) systems has become in widespread. PV systems operating under partial shading conditions (PSCs) influences its profitability. Under PSC, PV system exhibit multiple local maximum power points (LMPPs) and one global maximum power point (GMPP). This work aims to study the performance and comparative analysis of 4 popular metaheuristic MPP algorithms under multiple partial shading conditions. The techniques studied are Flower Pollination Algorithm (FPA), Cuckoo Search Optimization Algorithm (CSO), Grey Wolf Optimization Algorithm (GWO), and Particle Swarm Optimization Algorithm (PSO). The tracking performance is evaluated under fast changing 5 shading scenarios. Results show that all the metaheuristic algorithms are found to have fast response and able to converge to their final steady state value within a short time of nearly 0.1s. However, for diverse shading scenarios, different algorithms are found to perform better. Thus a unique metaheuristic algorithm is not optimal for all shading scenarios. The energy yield calculations is proposed as a basis for optimal algorithm selection. GWO is identified as the optimal algorithm with the best energy output when shading scenarios have equal duration.