Hybrid Atom Search and Salp Swarm Optimization for Parameter Estimation in Solar Photovoltaic Modules

Abstract

The efficient design of solar photovoltaic (PV) modules relies on accurately estimating the internal parameters of their equivalent circuit models. This task involves solving highly nonlinear and multimodal optimization problems. To address this challenge, this paper proposes a hybrid metaheuristic approach (ASOSSA) that integrates Atom Search Optimization (ASO) with Salp Swarm Algorithm (SSA) to improve parameter estimation for single and double diode PV models. By combining ASO’s global search capabilities with SSA’s dynamic position updates, the proposed method enhances convergence and avoids local optima. Simulation results, benchmarked against various state-of-the-art algorithms, demonstrate ASOSSA’s effectiveness and robustness in producing precise parameter estimates, even under noisy measurement conditions.