New MPPT hybrid controller based on genetic algorithms and particle swarm optimization for photovoltaic systems

Abstract

Traditional Maximum Power Point Tracking (MPPT) techniques are unable to reach high performance in photovoltaic (PV) system under partial shading conditions because of the multi-peaks present in the Power-Voltage curve. For that, particle Swarm Optimization (PSO) and genetic algorithms (GA) have been combined in recent years. However, these algorithms demonstrate some drawbacks in tracking accuracy and convergence rates, which impair control performance. In this paper, a new controller based on hybridization of PSO and GA is introduced to track the global maximum power point (GMPP). The proposed algorithm (HPGA) increases the balance rate between exploration and exploitation due to the cascade design of GA and PSO. Thus, the GMPP tracking of both algorithms will be improved. Simulations are carried out based on ISOFOTON-75W PV modules to prove the high performance of the proposed algorithm. From the obtained results, we conclude that HPGA shows fast convergence and very good tracking accuracy of GMPP in PV system even under different shading patterns.