Enhancing Power Quality with Intelligent Control: Neural Network-Driven Shunt Active Power Filter for Harmonic Mitigation

Abstract

This project addresses the issue of harmonic distortion in power systems caused by non-linear loads, such as rectifiers, induction motors, and unbalanced loads, by designing and implementing an ANN-based Shunt Active Power Filter (SAPF). Harmonics, which are voltage or current waveforms at multiples of the fundamental frequency, can severely impact power quality, leading to reduced efficiency, overheating, and potential equipment damage. The SAPF developed in this project successfully reduces the Total Harmonic Distortion (THD) in the source current from 27.36% to 4.74% by generating compensating currents that neutralize the harmonics introduced by these non-linear loads. Initially, the SAPF was based on a PI Controller, achieving a THD reduction to 4.74%. However, by replacing the PI Controller with an Artificial Neural Network (ANN) in the SAPF, the THD was further reduced to 4.68%. The design of the SAPF includes a PQ and I Compensation Calculation subsystem for determining compensating currents, a Hysteresis Controller for generating switching signals, and a PQ Measurement subsystem for monitoring power quality. Each of these components contributes to the SAPF's ability to effectively mitigate harmonics and improve overall power quality. The project demonstrates the SAPF's robustness and effectiveness in real-world applications, making it a practical solution for systems where harmonic distortion poses significant challenges.