Hybrid Fuzzy Logic and Shading-Aware Particle Swarm Optimization for Dynamic Photovoltaic Shading Faults Mitigation

TL;DR

This paper presents a hybrid fuzzy logic and shading-aware particle swarm optimization framework to improve maximum power point tracking in photovoltaic systems under partial and complete shading conditions, enhancing efficiency and reliability.

Contribution

It introduces a novel hybrid optimization approach combining fuzzy logic control with shading-aware PSO for dynamic PV shading fault mitigation, outperforming traditional methods.

Findings

Up to 11.8% increase in power output.

62% reduction in MPPT tracking time.

Effective handling of partial and complete shading scenarios.

Abstract

Shading faults remain one of the most critical challenges affecting photovoltaic (PV) system efficiency, as they not only reduce power generation but also disturb maximum power point tracking (MPPT). To address this issue, this study introduces a hybrid optimization framework that combines Fuzzy Logic Control (FLC) with a Shading-Aware Particle Swarm Optimization (SA-PSO) method. The proposed scheme is designed to adapt dynamically to both partial shading (20%-80%) and complete shading events, ensuring reliable global maximum power point (GMPP) detection. In this approach, the fuzzy controller provides rapid decision support based on shading patterns, while SA-PSO accelerates the search process and prevents the system from becoming trapped in local minima. A comparative performance assessment with the conventional Perturb and Observe (P\&O) algorithm highlights the advantages of the hybrid model, showing up to an 11.8% improvement in power output and a 62% reduction in tracking time. These results indicate that integrating intelligent control with shading-aware optimization can significantly enhance the resilience and energy yield of PV systems operating under complex real-world conditions.