Adaptive evolutionary jellyfish search algorithm based optimal photovoltaic array reconfiguration under partial shading condition for maximum power extraction

Abstract

This paper proposes an adaptive evolutionary jellyfish search algorithm (AEJSA) to optimally reconfigure photovoltaic (PV) array under partial shading condition (PSC) for real-time maximum power extraction. Jellyfish search algorithm (JSA) is selected owing to its effectiveness for real-time optimization. Besides, a series of discrete operations are performed on JSA to solve the discrete optimization problem of PV array reconfiguration. Due to the inherent drawback of JSA that it is easy to trap at the local optimal solution, an adaptive threshold for changing search mechanism is adopted to balance the local exploration and global exploitation. If the number of times that the value of objective function keeps unchanged exceeds this threshold, three operations (exchange, moving, and inver-over) will be implemented on the whole population for a wide global exploitation. In addition, to verify the feasibility of the hardware implementation of AEJSA, a hardware-in-the-loop test on a RTLAB platform is employed. Eleven meta-heuristic algorithms are applied and compared to AEJSA under objective PSC and subjective PSC to evaluate the optimized performance of AEJSA under various shadow conditions. The simulation results show that the mismatched power loss obtained by AEJSA is smallest, which reduced by 7.26% against gravitational search algorithm.