DESIGN & SIMULATION OF SOLAR PANEL FED CASCADED H BRIDGE MULTILEVEL INVERTER USING ANN CONTROLLER

Abstract

The increasing dependence on solar energy demands advanced power electronic interfaces capable of delivering highquality AC power to electrical loads and utility grids. Multilevel inverters are an effective solution due to their ability to synthesize near-sinusoidal waveforms with reduced switching stress. This work focuses on the simulation and design of a solar photovoltaic-fed cascaded H-bridge multilevel inverter employing an Artificial Neural Network (ANN)- based control technique. Independent DC sources derived from solar panels are utilized to generate multiple voltage levels, enhancing output voltage quality while minimizing harmonic distortion. The ANN controller is trained to determine appropriate switching states, allowing the inverter to adapt to nonlinear system behavior and variations in solar irradiance and load conditions. The proposed system is developed and analyzed using MATLAB/Simulink, and its performance is evaluated in terms of output voltage stability, harmonic profile, and dynamic response. Simulation results indicate that the ANN-controlled cascaded H-bridge inverter provides improved harmonic suppression and faster dynamic performance compared to conventional control methods, demonstrating its effectiveness for renewable energy conversion applications.