An Improved Hunger Games Search Algorithm-based Multi-peak MPPT Control for PV System under Partial Shading

Abstract

In photovoltaic power generation systems, partial shading may cause the PV array to mismatch, thus leading to multi-peak output characteristics, which makes the conventional Maximum Power Point Tracking (MPPT) algorithm easily fall into local extremes and cause power loss. The study aimed to accurately and quickly track the maximum power point of PV arrays in partial shading through swarm intelligence algorithms. Based on the above, an MPPT control algorithm based on Chaos Adaptive Hunger Games Search with Dynamic Lévy Mutation Strategy (CAHGSL) is proposed in this paper. By introducing an improved logistics chaos map initialization population, a nonlinear adaptive convergence factor and a dynamic Lévy mutation strategy enhance their ability to jump out of local extremes during multi-peak MPPT and improve their tracking speed and efficiency. Under the three working conditions, the tracking efficiency of the MPPT algorithm proposed in this paper has been achieved by more than 99.5% in an average time of 0.152s, which is higher tracking efficiency compared to the PO, PSO, and HGS algorithms. The results show that the MPPT algorithm proposed in this paper can balance the tracking speed and efficiency with less power oscillation during the tracking process, and can ensure stable output after convergence. The method proposed in this paper is helpful to improve the output power of PV arrays under partial shading.