Application of hippopotamus optimization algorithm based maximum power point tracking for photovoltaic systems operating under partial shading conditions

Hippopotamus Optimization Algorithm (HOA) represents a recently developed metaheuristic algorithm, demonstrating remarkable robustness and effectiveness in resolving complex problems. One of the central challenges in the design of photovoltaic (PV) systems lies in optimizing the tracking of the maximum power point (MPP). To improve the overall efficiency of PV panels, the implementation of a robust MPP tracking (MPPT) method is essential. In contrast, the performance of PV systems can be significantly degraded under partial shading conditions because traditional algorithms often converge to a local MPP rather than identifying the global MPP (GMPP). So, to tackle this issue, this article presents an innovative integration of a new algorithm for MPPT control strategy, based on the HOA method, to overcome the weaknesses of current techniques for PV systems. The proposed technique is characterized by improved overall performance, rapid and efficient localization of the GMPP, and stabilization with insignificant oscillations around this point. Furthermore, a quantitative comparison and statistical analysis, including classical optimization techniques (perturb and observe, incremental conductance), is conducted on several case scenarios. The feasibility and performance of the proposed MPPT method, based on HOA optimization, are evaluated through MATLAB Simulink.