Intelligent fuzzy logic-controlled space vector PWM three-phase inverter with MPPT for dynamic solar energy systems

Abstract

The integration of intelligent control strategies in photovoltaic (PV) systems has become essential for optimizing energy extraction and ensuring stable operation under dynamic environmental conditions. This study presents an Intelligent Fuzzy Logic-Controlled Space Vector Pulse Width Modulation (SVPWM) Three-Phase Inverter integrated with a Maximum Power Point Tracking (MPPT) algorithm for dynamic solar energy systems. The primary objectives were to design a fuzzy logic-based MPPT controller for rapid and accurate tracking of the maximum power point, implement SVPWM for precise inverter control with reduced Total Harmonic Distortion (THD), and validate the system’s performance under dynamic environmental variations. The methodology involved modeling the PV array and designing an MPPT controller using fuzzy logic principles to dynamically adjust the duty cycle of a DC-DC boost converter. An SVPWM control strategy was applied to the three-phase inverter for efficient power modulation and harmonic reduction. The system was simulated in MATLAB/Simulink, followed by hardware validation using a Digital Signal Processor (DSP)-based prototype. Results demonstrated an average MPPT efficiency of 98.7%, significantly outperforming traditional Perturb and Observe (P&O) (94.5%) and Incremental Conductance (IC) (95.8%) techniques. The inverter achieved a 2.5% THD, significantly lower than the 4.8% THD observed with conventional Sinusoidal Pulse Width Modulation (SPWM). The transient response analysis revealed a 30% reduction in response time and a steady-state error of 0.2%, ensuring system stability under fluctuating solar irradiance. Statistical validation using ANOVA and regression analysis confirmed significant improvements (p< 0.001) across all performance metrics. The study concludes that integrating fuzzy logic-based MPPT with SVPWM inverters enhances efficiency, stability, and adaptability in PV systems. Practical recommendations include adopting intelligent control systems, periodic calibration, and exploring hybrid AI-based MPPT techniques for further improvements.