Research on Photovoltaic MPPT Under Complex Conditions Based on an Advanced Great Wall Construction Algorithm Method

Abstract To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. The method, named LGWGWCA-P&O, combines the enhanced Great Wall Construction Algorithm (GWCA) with the Perturbation and Observation (P&O) technique, providing a robust solution for addressing PV MPPT challenges. The LGWGWCA-P&O method enhances the GWCA by integrating a positional update strategy from the Grey Wolf Algorithm (GWO) for the optimized distribution of solution agents. Moreover, it incorporates a Levy flight strategy to mitigate the issue of excessive randomness during the replacement and recombination of worker individuals, thereby enhancing the speed of tracking. Furthermore, as the algorithm nears the maximum power point of PV power generation, it transitions to the perturbation observation approach to capitalize on its swift convergence capability, thereby facilitating a quicker and more precise identification of the maximum power point.