Comparison of Two Hybrid Global Maximum Power Point Algorithms for Photovoltaic Module under Both Uniform and Partial Shading Condition

Abstract

Power vs. Voltage (P-V) characteristics of a photovoltaic module (PV) show multiple peaks under partial shading conditions (PSCs). Most conventional maximum power point tracking (MPPT) techniques can accurately locate the single point under uniform conditions but fail under PSCs. Intelligent algorithms can locate the global point (GMPP) among the local ones (LP) but incur more computational cost. Combining both types as hybrid GMPPT provides more effective performance under different environmental conditions. This paper aims to analyze and compare the performance of two hybrid GMPP techniques under both uniform conditions and partial shading. In the proposed approach, the genetic algorithm (GA) and particle swarm optimization (PSO) are integrated with the perturb and observe algorithm (P&O). The simulation results in Matlab/Simulink confirm that both hybrid algorithms can track the GMPP. Furthermore, they show the ability to differentiate between different environment changes occurrences