Hybrid MPPT-based PV-Integrated Three-Phase Grid-Tied Inverter Using Adaptive Particle Swarm Optimization Technique

This paper presents an intelligent control technique for a three-phase inverter system that integrates a photovoltaic (PV) system, aiming to enhance power extraction efficiency, grid compliance, and dynamic response under variable environmental conditions. The proposed Maximum Power Point Tracking (MPPT) approach combines Flying Squirrel Search Optimization (FSSO) with Grey Wolf Optimization (GWO) to ensure rapid global convergence and stable local tracking of the maximum power point, particularly during fluctuating irradiance and temperature. In parallel, an Adaptive Particle Swarm Optimization (APSO) algorithm is employed to dynamically tune the modulation index of the inverter, minimizing voltage error and stable frequency in the output. The integrated system is modeled and simulated in MATLAB/Simulink, and its performance is evaluated based on metrics such as tracking accuracy, settling time, overshoot, and grid stability. Simulation results demonstrate that the hybrid control framework outperforms conventional MPPT and inverter techniques, delivering improved grid synchronization, faster convergence, and high-quality sinusoidal output. This research contributes toward developing intelligent, adaptive control solutions for next-generation smart grid-connected PV systems.