A novel population based maximum point tracking algorithm to overcome partial shading issues in solar photovoltaic technology

Abstract

Maximum power point (MPP) tracking (MPPT) is a crucial aspect of photovoltaic (PV) technology for operating in an optimum location throughout the day. The bypass diodes are connected across series connected PV modules to avoid the hotspot phenomenon resulting in multiple peaks in the power-voltage (P-V) curve during partial shading conditions. Under this situation, tracking of global MPP (GMPP) for PV system by conventional approach is incompetent. Thus, a global MPPT controller is designed based on a novel population based algorithm, student psychology based optimization (SPBO), to enhance the overall performance of the 4S and the 3S configurations of the PV array. The effectiveness and the feasibility of SPBO algorithm for catching the GMPP are verified under several shadow arrangements. For proving effectiveness, the simulation results of SPBO are compared with human behaviour based optimization, improved chaotic particle swarm optimization (PSO), PSO, fuzzy logic control and teaching–learning based optimization. The proposed SPBO algorithm is able to successfully catch the GMPP under different weather scenarios and exhibits superior performance in terms of iteration, tracking time and efficiency. However, the temperature variation marginally affects the tracking efficiency of the SPBO method. This analysis is performed on the 4S configuration of the PV array. For better justification of the concept of stability, statistical analysis is also conducted on the 3S configuration of PV array separately.