Experimental validation of novel hybrid Grey Wolf Equilibrium Optimization for MPPT to improve the efficiency of solar photovoltaic system

This paper introduces a novel strategy for tracking the Maximum Power Point (MPP) using Grey Wolf Equilibrium Optimizer (GWEO) algorithm, which integrates a search mechanism of Grey Wolf Optimizer (GWO) and Equilibrium Optimizer (EO). This combination enhances both the search capabilities and convergence speed of the algorithm. The proposed GWEO algorithm's effectiveness is evaluated under diverse and challenging conditions, including Standard Test Conditions (STCs) and Partial Shading Conditions (PSCs), to assess its ability to conduct global and local searches efficiently. Performance tests were carried out for STC scenario and four PSC scenarios, with the results compared against four well-established MPPT algorithms: Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), GWO, and Flower Pollination Algorithm (FPA). The GWEO method demonstrated its superiority by achieving an average tracking time of 0.9s with 99.96% efficiency under STCs and 0.975s with 99.95% efficiency under PSCs. The algorithm's efficiency and reliability were further validated through an experimental setup involving a DSPACE board and a PV emulator, particularly under complex PSCs. These results highlight the potential of GWEO in improving the efficiency and sustainability of PV systems in practical energy applications.