Metaheuristic Optimization of Maximum Power Point Tracking in PV Array under Partial Shading

Abstract

Optimal energy harvesting is dependent on the efficient extraction of energy from photovoltaic (PV) arrays. Maximum Power Point Tracking (MPPT) algorithms are crucial in achieving the maximum power harvest from the PV systems. Therefore, in response to a fluctuating power generation rate due to shading of the PV, the MPPT algorithms must dynamically adapt to the PV array's Maximum Power Point (MPP). In this article, three metaheuristic optimization MPPT techniques, utilized in DC converters connected to the array of 4 PV panels, are compared. The Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are used to optimize MPPT in the converter, are compared. This research evaluates the efficiency of each optimization method in converging to MPP under 2 s after partial shading of the PV with respect to velocity and accuracy. All algorithms exhibit fast MPPT optimization. However, among the evaluated algorithms, the PSO was distinguished for its higher stability and efficiency.

Keywords

• MPPT optimization
• photovoltaics
• metaheuristics
• convergence evaluation
• partial shading