Disturbance pattern recognition method of power quality based on the fuzzy clustering analysis

Authors

Xiangqun CHEN, Power Supply Service Center(Metrology Center),State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004 ,China ；Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004 ,China
Maotao YANG, Power Supply Service Center(Metrology Center),State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004 ,China ；Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004 ,China
Mouhai LIU, Power Supply Service Center(Metrology Center),State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004 ,China ；Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004 ,China
Rui HUANG, Power Supply Service Center(Metrology Center),State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004 ,China ；Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004 ,China
Minqi YU, Power Supply Service Center(Metrology Center),State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004 ,China ；Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004 ,China
Zhi WANG, Power Supply Service Center(Metrology Center),State Grid Hunan Electric Power Co.,Ltd.,Changsha 410004 ,China ；Province Key Laboratory of Intelligent Electrical Measurement and Application Technology,Changsha 410004 ,China

Keywords

power quality disturbance, HHT, fuzzy clustering, pattern recognition, voltage mutation

Abstract

In order to improve the accuracy of power quality disturbance recognition and make up for the shortcomings of traditional single feature quantity pattern recognition methods that are easily disturbed and have low precision, a power quality disturbance pattern recognition method based on fuzzy cluster analysis is proposed. The method uses Hilbert-Huang transformation (HHT) to extract corresponding disturbance feature quantities from various types of power quality disturbance signals, and then performs fuzzy clustering analysis on the extracted feature quantities to accurately classify these power quality disturbance signals into photovoltaic disturbances and public grid disturbances one by one. At the same time, a power quality disturbance identification process based on fuzzy cluster analysis is established. Simulation results show that this method overcomes the limitations of the traditional single-feature pattern recognition method, optimizes the recognition effect of disturbance signals, improves the recognition efficiency, and has high recognition accuracy and strong anti-noise ability.

DOI

10.19781/j.issn.1673-9140.2022.02.010

First Page

79

Last Page

85

Recommended Citation

CHEN, Xiangqun; YANG, Maotao; LIU, Mouhai; HUANG, Rui; YU, Minqi; and WANG, Zhi (2022) "Disturbance pattern recognition method of power quality based on the fuzzy clustering analysis," Journal of Electric Power Science and Technology: Vol. 37: Iss. 2, Article 10.
DOI: 10.19781/j.issn.1673-9140.2022.02.010
Available at: https://jepst.researchcommons.org/journal/vol37/iss2/10