Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions

Abstract

Photovoltaic (PV) systems are adversely affected by partial shading and non-uniform conditions. Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots. It also produces numerous peaks in the PV array’s power-voltage characteristics, thereby trapping conventional maximum power point tracking (MPPT) methods in local peaks. Swarm optimization approaches can be used to address this issue. However, these strategies have an unreasonably long convergence time. The Grey Wolf Optimizer (GWO) is a fast and more dependable optimization algorithm. This renders it a good option for MPPT of PV systems operating in varying partial shading. The conventional GWO method involves a long conversion time, large steady-state oscillations, and a high failure rate. This work attempts to address these issues by combining Cuckoo Search (CS) with the GWO algorithm to improve the MPPT performance. The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe (P&O). A comparative analysis reveals that under non-uniform operating conditions, the hybrid GWO CS (GWOCS) approach presented in this article outperforms the GWO and P&O approaches.