Enhanced Efficiency in Dual-Stage Grid-Connected Photovoltaic Systems with PSO-Integrated Hybrid MPPT Techniques

Abstract

Maximum power point tracking (MPPT) is essential for maximizing photovoltaic (PV) energy conversion under variable irradiance and temperature. Conventional perturb and observe (P&O) and incremental conductance (INC) algorithms are simple to implement but may exhibit slow convergence, steady-state oscillations, and reduced tracking accuracy during rapid environmental changes. This study proposes two hybrid MPPT controllers—particle swarm optimization (PSO)-assisted P&O and PSO-assisted INC for a dual-stage grid-connected PV system. PSO adaptively determines the perturbation step size, while bounded duty-cycle constraints, transient PSO execution, and reduced perturbation near the maximum power point are applied to limit control jitter and improve stability. The proposed controllers were evaluated in MATLAB/Simulink under time-varying irradiance and temperature and compared with conventional P&O and INC methods. Both hybrid approaches improved power tracking, reduced oscillations at the DC–DC boost converter output, and maintained stable grid synchronization. The PSO–P&O controller achieved 97% efficiency, whereas PSO–INC reached 98.5%, demonstrating superior tracking accuracy and steady-state performance. These findings indicate that combining global PSO search with deterministic local tracking can enhance the efficiency, robustness, and reliability of grid-connected PV systems. Future work should validate the controllers experimentally under partial shading, grid disturbances, and real-time hardware conditions.