Critical Analysis and Application of Enhanced Hunter-Prey Algorithm for MPPT in Photovoltaic Systems Under Complex Partial Shading Conditions

Abstract

The Hunter Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional Maximum Power Point Tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in Improved Hunter Prey Optimization (IHPO) is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. The performance evaluation is conducted utilizing both MATLAB simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. Simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions with a tracking response time of approximately 0.2 seconds. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method's output performance is evaluated on the StarSim RCP platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.