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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: email

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

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

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APA Style
Yan, Y., Chen, K., Geng, H., Fan, W., Zhou, X. (2023). A review on intelligent detection and classification of power quality disturbances: trends, methodologies, and prospects. Computer Modeling in Engineering & Sciences, 137(2), 1345-1379. https://doi.org/10.32604/cmes.2023.027252
Vancouver Style
Yan Y, Chen K, Geng H, Fan W, Zhou X. A review on intelligent detection and classification of power quality disturbances: trends, methodologies, and prospects. Comput Model Eng Sci. 2023;137(2):1345-1379 https://doi.org/10.32604/cmes.2023.027252
IEEE Style
Y. Yan, K. Chen, H. Geng, W. Fan, and X. Zhou "A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects," Comput. Model. Eng. Sci., vol. 137, no. 2, pp. 1345-1379. 2023. https://doi.org/10.32604/cmes.2023.027252



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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