Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm

This work presents an alternative to the conventional photovoltaic maximum power point tracking (MPPT) methods, by using opposition-based learning firefly algorithm (OFA) that improves performance of Photovoltaic (PV) system both in uniform irradiance changes and partial shading conditions. The is based on fireflies’ search for food, according which individuals emit progressively more intense glows as they approach objective, attracting other fireflies. Therefore, simulation this behavior can be conducted solving objective function directly proportional distance from desired result. To implement case conditions, it was necessary adjust Firefly Algorithm (FA) parameters fit MPPT application. These have been extensively tested, converging satisfactorily guaranteeing extract global (GMPP) cases normal conditions analyzed. precise adjustment coefficients made possible visualizing movement particles during convergence process, while (OBL) used with FA accelerate process allowing particle move opposite direction. proposed simulated closest way authentic operating variable were implemented experimentally a 60 [W] PV system. A two-stage grid-connected designed deployed validate algorithm. In addition, comparison between Perturbation Observation (P&O) method carried out prove effectiveness over methods GMPP.