Near-Perfect Time–Frequency Analysis of Power Quality Disturbances

Abstract

Time-frequency analysis is a key requirement for the detection of power quality disturbances (PQDs). Due to non-stationary nature of the disturbances and resolution restriction posed by the uncertainty principle, most signal processing techniques fail to single-handedly detect different types of disturbances with desired precision. As a solution to this problem, a near-perfect time-frequency analysis technique is presented for PQDs. It utilizes the concept of instantaneous frequency (IF)-based time–frequency representations. A carefully chosen Gaussian window in the windowed Fourier transform followed by the slight post-processing gives a high spectral concentration on the IF trajectory by eliminating unwanted frequencies. Several comparative case studies are shown, which support the effective detection and classification capabilities offered by the proposed technique.

KEYWORDS:

• Detection
• Power quality
• Power quality disturbances
• S-transform
• Short-time Fourier transform

ACKNOWLEDGMENT

The author thanks Dr Leandro Di Persia (Universidad Nacional del Litoral, Argentina), Dr Gang Yu (University of Jinan, China), Dr Abdelhalim Zekry (Ain Shams University, Egypt), Dr Fernando Soares Schlindwein (University of Leicester, UK), and Dr. Colton Magnant (Georgia Southern University, USA). Technical discussions with them have helped in preparing this manuscript.

Additional information

Notes on contributors

Utkarsh Singh

Utkarsh Singh received a bachelor's degree in electrical and electronics engineering from Uttar Pradesh Technical University, India, in 2012. He completed his master's degree with AICTE fellowship in power systems from Thapar University, India, in 2014. He completed his PhD with MHRD fellowship in power quality from the Indian Institute of Technology Roorkee in February 2018. From June 2018 to January 2020, he worked as a postdoctoral researcher in the OPERA-Wireless Communications Group at Université libre de Bruxelles, Belgium, on project “MUFINS” funded by INNOVIRIS. Since February 2020, he is working as a postdoctoral researcher in the Artificial Intelligence Lab at Vrije Universiteit Brussels. His research interests include power quality, signal processing, artificial intelligence, data analysis, and optimization.