Adaptive fuzzy logic-controlled space vector PWM with dynamic MPPT for high-efficiency solar PV inverter systems

Abstract

This study presents an Adaptive Fuzzy Logic-Controlled Space Vector PWM (AFLC-SVPWM) system integrated with dynamic Maximum Power Point Tracking (MPPT) for high-efficiency solar photovoltaic (PV) inverter systems. The primary objective was to address limitations of conventional MPPT methods, such as slow convergence, oscillations around the maximum power point (MPP), and suboptimal inverter performance under rapidly changing environmental conditions. The experimental setup included a monocrystalline solar PV array, a three-phase inverter with SVPWM control, and an adaptive fuzzy logic controller implemented on a DSP platform (TMS320F28335). MATLAB/Simulink was used for system modeling, and performance was validated through both simulation and real-time experiments. The study analyzed key performance metrics, including MPPT efficiency, total harmonic distortion (THD), dynamic response time, and overall system efficiency. Results revealed that the AFLC-SVPWM system achieved MPPT efficiencies up to 99%, significantly outperforming conventional P&O and INC methods. Additionally, the inverter demonstrated lower THD values (2.1%-3.0%) and faster response times (210-250 ms) under dynamic irradiance conditions. Statistical analyses, including ANOVA and paired t-tests, confirmed the robustness and reliability of the proposed system. The adaptive fuzzy logic controller dynamically optimized switching strategies, ensuring consistent performance across varying load and environmental conditions. In conclusion, the AFLC-SVPWM system offers a highly efficient, responsive, and reliable control solution for solar PV inverters. Practical recommendations include integrating AI-based control enhancements, scaling for large PV installations, and conducting extensive field trials to validate system scalability and reliability.