Maximum Power Tracking System for Photovoltaic Power Generation in Local Shadow Environment Based on Ant Colony Optimization Fuzzy Algorithm

Abstract

INTRODUCTION: Photovoltaic power generation, as a rapidly developing new energy technology, is increasingly receiving attention from countries around the world.  However, the efficiency of photovoltaic power generation systems is influenced by various factors.  Local shadows have become one of the bottlenecks restricting the development of photovoltaic systems. OBJECTIVES: The research aims to improve the maximum power tracking performance of photovoltaic systems under local shadow conditions. METHODS: A maximum power tracking system based on ant colony optimization fuzzy algorithm is proposed. Research can effectively solve local optimal problems caused by local shadows through ant colony algorithm. Combining fuzzy algorithms can not only improve the tracking accuracy of the maximum power tracking system, but also enhance the adaptability to complex environments. RESULTS: In the simulation experiment results, the error between the ant colony optimization fuzzy algorithm and the actual maximum power in four local shadow environments was 0.21W, 0.55W, 0.27W, and 0.98W, respectively. Both stability and accuracy were superior to ant colony algorithm, fuzzy algorithm, and perturbation observation method. CONCLUSION: Research has confirmed the potential value of ant colony optimization fuzzy algorithm in maximum power tracking of photovoltaic power generation, providing a new solution for the operation and management of photovoltaic power plants.