Enzyme Action Optimization–Based Maximum Power Point Tracking for Solar Photovoltaic Systems

Abstract

This paper presents a novel maximum power point tracking (MPPT) technique for solar photovoltaic systems based on the enzyme action optimization (EAO) algorithm, inspired by catalytic enzyme behavior in biological processes. The proposed EAO-based MPPT approach effectively balances exploration and exploitation to ensure rapid and accurate tracking of the global maximum power point under both uniform and partial shading conditions. The algorithm is first implemented in MATLAB and evaluated across seven test scenarios representing various irradiance patterns. Its performance is benchmarked against conventional perturb and observe, particle swarm optimization, salp swarm optimization, and hybrid particle swarm–genetic optimization algorithms. To validate real-time applicability, hardware-in-the-loop simulations are performed using the OPAL-RT platform under different operating conditions. The results demonstrate that the EAO algorithm achieves faster convergence, reduced oscillations, and higher tracking efficiency compared to existing methods. Specifically, the proposed EAO-based MPPT achieves an average tracking efficiency of 99.02% across all seven test scenarios, outperforming PSO (98.58%), SSA (93%), and P&O (80.55%) methods. The algorithm successfully tracks the global maximum power point within 0.34 s under partial shading conditions and exhibits negligible steady-state power oscillations. Hardware-in-the-loop validation on the OPAL-RT platform under four different irradiance patterns confirms an average tracking efficiency exceeding 99%, demonstrating the practical applicability and robustness of the proposed approach.