Enhanced power extraction in shaded photovoltaic arrays using dynamic reconfiguration with multi-objective nutcracker optimization algorithm

Photovoltaic (PV) array performance is notably affected by Partial Shading Conditions (PSCs) by causing mismatch losses, reducing energy output, and compromising system reliability. The proposed study outlines a dynamic reconfiguration strategy for PV arrays under PSC using the Multi-Objective Nutcracker Optimization Algorithm (MONOA). MONOA is a recently developed bio-inspired metaheuristic designed for multi-objective optimization problems. It integrates fast opposition-based learning, crowding distance and non-dominated sorting to effectively search for optimal PV switching configurations that maximize output power and minimize current imbalance and switching activity. A Total Cross-Tied (TCT) based 9 × 9 PV array is used as test system, and a novel Objective Function (OF) is proposed for enhancing solution quality without requiring weight tuning. The performance of MONOA is compared against well-established algorithms such as Improved Prairie Dog Optimization (IPDO), Atom Search Optimization (ASO), and African Vultures Optimization Algorithm (AVOA) under two different shading patterns. Simulation results demonstrate that MONOA consistently achieves higher power output, better fill factor, lower mismatch loss, and the fastest execution time among all tested methods.