A New Equilibrium Walrus Optimizer-Based Tracker for Enhancing the Generation of Photovoltaic System Under Partial Shade

Abstract

Partial shading (PS) conditions introduce multiple local maxima in power-voltage characteristic of photovoltaic (PV) arrays, significantly degrading the performance of conventional maximum power point tracking (MPPT) techniques. To address this challenge, this paper proposes an equilibrium walrus optimizer (EWO)-based MPPT strategy, which enhances the recently developed walrus optimizer (WO) by incorporating an equilibrium-bool exploration mechanism and adaptive control parameter regulation. These modifications substantially improve the balance between exploration and exploitation, enabling reliable global peak (GP) tracking in highly multimodal search spaces. The proposed EWO dynamically adjusts the duty cycle of dc-dc boost converter connected to 4 × 1 PV array, ensuring maximum power extraction under partial shading conditions. The effectiveness of the EWO is first validated using the CEC’20 benchmark suit, where it demonstrates faster convergence and superior solution quality compared to several state-of-the-art metaheuristic optimizers. Subsequently, three realistic partial shading scenarios as well as time-varying irradiance pattern are examined, and the EWO-based MPPT is compared to grey wolf optimizer (GWO), particle swarm optimizer (PSO), sine cosine algorithm (SCA), equilibrium optimizer (EO), and original WO. Simulation results confirm that the proposed EWO consistently tracks the global peak with efficiencies of 99.92172%, 99.9097%, and 99.88653%, outperforming all comparative techniques in terms of tracking accuracy, convergence speed, and steady-state stability. These results demonstrate that the proposed tracker offers robust and efficient solution for PV systems operating under complex partial shading conditions.