Abstract
Partial shading conditions generate multiple peaks in the P-V curve of photovoltaic (PV) arrays. Smart MPPT optimization techniques should be used to capture the global peak (GP) and avoid being trapped in one of the local peaks (LPs). The tracking of the GP should be fast and reliable to enhance the stability and increase the generated efficiency of the PV systems. Bat algorithm (BA) is one of the fastest swarm optimization techniques. The BA control parameters (BA-CPs) have substantial effects on their performance. This paper introduced a nested BA strategy called BA-BA strategy to determine the optimal values of control parameters of BA for the lowest convergence time and failure convergence rate to be used in the online MPPT of PV systems. The inner BA loop used the BA as an MPPT of the PV system, meanwhile, the outer BA loop used the inner BA loop as a fitness function to determine the optimal BA-CPs for minimum convergence time and failure rate. Ten benchmark BA strategies, particle swarm optimization (PSO), and cuckoo search (CS) algorithm have been used to compare their results with the results obtained from the BA-BA strategy. The results of the BA-BA strategy reduced the convergence time of 250% of the time associated with the best benchmark BA strategy, 518%, and 395% as compared to the PSO, and CS algorithm, respectively. The simulation and experimental results obtained from the BA-BA strategy showed its superior for determining the optimal control parameters for BA in MPPT of PV systems or any other applications.