A Hybrid Particle Swarm Optimization-Fuzzy Logic Maximum Power Point Tracking Strategy for Photovoltaic Systems Under Uniform and Partial Shading Conditions
Keywords:
Particle Swarm Optimization, Fuzzy Logic, Partial shading, Renewable energy, DC boost converter, Photovoltaic systemsAbstract
Maximum power point tracking (MPPT) is an essential control technique of PV system which optimizes energy extraction from the PV panel according to the fluctuating environmental conditions. These traditionally used methods including Perturb and Observe (P&O) and Incremental Conductance (INC) has two major disadvantages; one is power oscillations and the other are not efficient under partial shading 1. In this paper, we present a new MPPT hybrid PSO-FL (Particle Swarm Optimization-Fuzzy Logic) algorithm that integrates the global search capability of PSO with an adaptive linguistic control that is based on fuzzy logic. To validate the proposed method, it is tested with a KC200GT PV module model presented with different irradiance levels (200-1000 W/m2) and five cases of partial shading. Simulation results show that the Hybrid PSO-FL can be as efficient as 99.43% tracking efficiency, 0.05 s overall convergence time, 0.97% THD and only a 0.57% power loss in steady state under uniform … By conducting a comparative analysis with P&O, INC, Fuzzy Logic, Grey Wolf Optimizer (GWO), PSO and Artificial Neural Network (ANN), we prove the superiority of the proposed approach. These observations are further supported by statistical validation through one-way ANOVA (F = 142.7) and Wilcoxon signed-rank tests (p < 0.001).
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Copyright (c) 2026 Ioannis Adamopoulos, Niki Syrou, Jovanna Adamopoulou, Antonios Valamontes, Konstantina Diamanti, Panagiotis Tsirkas, Harshit Mishra (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
