Open Access

REVIEW

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yanjun Yan, Kai Chen^*, Hang Geng, Wenqian Fan, Xinrui Zhou

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

* Corresponding Author: Kai Chen. Email:

Computer Modeling in Engineering & Sciences 2023, 137(2), 1345-1379. https://doi.org/10.32604/cmes.2023.027252

Received 21 October 2022; Accepted 28 January 2023; Issue published 26 June 2023

Abstract

With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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