Optimizing Power Quality in Electric Vehicles with AI-Driven Series Active Filters

Abstract

This paper explores the enhancement of power quality in electric vehicles (EVs) through the application of artificial intelligence (AI) in series active filters. As EVs become increasingly prevalent, maintaining high power quality is crucial for their efficient operation and longevity. Traditional methods of power quality management face limitations in adaptability and real-time performance. By integrating AI algorithms with series active filters, this research aims to develop a more responsive and intelligent system capable of dynamically adjusting to varying power conditions. The proposed approach leverages machine learning techniques to optimize filter performance, reduce harmonics, and improve overall system stability. Preliminary results demonstrate significant improvements in power quality metrics, showcasing the potential of AI-driven solutions in advancing EV technology. The demand for improving power quality, particularly in single-phase systems with various loads, has led to the simulation and analysis of a hybrid series active filter in the MATLAB environment. Unlike conventional setups, this hybrid configuration excludes a transformer in its circuit. The objective of this study is to address power quality issues and devise effective solutions, contributing to the mitigation of power-related problems. With a primary focus on power quality challenges associated with electrical vehicular transportation and diverse loads connected to the grid, this paper aims to provide insights into energy utilization and power quality optimization. The analysis centers on formulating a control strategy specifically tailored to reduce harmonic distortions in current waveforms, particularly concerning nonlinear and critical loads connected to the utility grid.