Performance Analysis of Solar PV Systems Under Partial Shading Conditions

Abstract

Solar photovoltaic (PV) systems are widely adopted as a sustainable energy solution; however, their performance is significantly affected by environmental factors, particularly partial shading conditions caused by clouds, trees, buildings, and dust accumulation. Partial shading leads to mismatch losses, hotspot formation, and reduced power output, thereby impacting overall system efficiency. This study presents a comprehensive performance analysis of solar PV systems under partial shading conditions to evaluate power degradation characteristics and mitigation strategies. The methodology involves modeling a photovoltaic array using MATLAB/Simulink under different shading scenarios, including uniform, partial, and dynamic shading patterns. Electrical parameters such as current-voltage (I–V) and power-voltage (P–V) characteristics are analyzed to understand the impact of shading on system behavior. Maximum Power Point Tracking (MPPT) techniques, including Perturb and Observe (P&O) and Incremental Conductance (INC), are implemented to enhance energy extraction under non-uniform irradiance conditions. The study also evaluates advanced configurations such as series-parallel, total-cross-tied (TCT), and bridge-linked array topologies to reduce mismatch losses. The results indicate that partial shading significantly reduces power output and introduces multiple local maxima in the P–V curve, making MPPT challenging. Among the evaluated methods, TCT configuration combined with advanced MPPT algorithms shows improved performance in mitigating shading effects. It is also observed that intelligent tracking methods enhance energy yield and system stability under rapidly changing environmental conditions. The study concludes that understanding and mitigating partial shading effects are essential for improving the reliability and efficiency of PV systems. These findings support the development of optimized solar energy systems for enhanced performance in real-world operating conditions.