Maximum Power Point Tracking in Photovoltaic Systems Using Particle Swarm Optimization Under Dynamic Environmental Conditions

Photovoltaic (PV) systems exhibit nonlinear current-voltage characteristics that vary significantly with irradiance and temperature, making conventional Maximum Power Point Tracking (MPPT) techniques such as Perturb & Observe (P&O) and Incremental Conductance (IncCond) prone to oscillation around the Maximum Power Point (MPP), slow convergence, and complete failure under partial shading or rapidly changing atmospheric conditions. This paper proposes an improved Particle Swarm Optimization (PSO)-based MPPT algorithm specifically tailored for real-world dynamic environments. The algorithm incorporates adaptive inertia weight, velocity clamping, and a re-initialization mechanism triggered by sudden irradiance changes to prevent the swarm from getting trapped in local optima during partial shading. Extensive simulation results using MATLAB/Simulink and experimental validation on a ۳ kW grid-connected PV array demonstrate that the proposed PSO-MPPT achieves tracking efficiency.

Keywords:

Photovoltaic system , Maximum Power Point Tracking (MPPT) , Particle Swarm Optimization (PSO) , partial shading , dynamic irradiance , global maximum power point