Empirical analysis of power quality using UPQC with hybrid control techniques

Abstract

The Unified Power Quality Conditioner (UPQC), composed of both parallel active filters (PAF) and series active filters (SAF), recognized as a crucial device for enhancing the overall power quality of the system. In context, different control methods are employed to evaluate the performance of UPQC. This study aims to explore the performance of four distinct control techniques: dq, pq, Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) in hybrid combinations to manage both PAF and SAF in UPQC device. Four different hybrid cases are developed for addressing three power quality problems: harmonic distortion, voltage sag, and swell to evaluate the performance of these control techniques. The results revealed a significant reduction in Total Harmonic Distortion (THD) for hybrid ANFIS-ANN control method, achieving a THD value of 4.26 % for load voltage and 0.08 % for source current after compensation in comparison with other methods. Furthermore, this paper concludes by recommending the most suitable combination of control techniques for specific applications.