AI-Driven DC-AC Converter for Efficient Power Conversion and Harmonic Reduction
Abstract
In the present work, an artificial intelligence (AI)-based direct current to alternating current converter is developed for fast power conversion with reduced harmonic distortion in three-phase power systems. The system that comprises a DC-link source, inverter switching network, pulse width modulation (PWM) control method, output filter and AI-based adaptive controller minimise voltage fluctuation and improves quality waveforms. An adaptive switching signal for inverter generation based on the error in voltage, rate of change of error and variations in power along with DC-link voltage is processed as a part of the control strategy. Mathematical modelling is established using inverter pole-voltage equations, neutral-point voltage compensation, Clarke and Park transformations, sinusoidal reference generation, the modulation index ratio to duty ratio for filter dynamics, and root mean square (RMS) estimation, along with total harmonic distortion (THD). The performance metric is a weighted sum of voltage tracking error, current variation and harmonic distortion, which aims to provide a stable low-distortion operation for the converter. Results of the simulation demonstrate accuracy in converter response, including input DC voltage, switching pulse patterns, unfiltered output voltage, filtered three-phase AC voltage and THD. The emulated DC-AC converter can be used for renewable energy systems, grid-connected inverter applications, electric vehicle power interfaces and various configuration units that require stable voltage output and reduced distortion.