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

doi:10.19781/j.issn.1673-9140.2022.02.010

Home > Archive>Volume 37, Issue 2, 2022 >79-85. DOI:10.19781/j.issn.1673-9140.2022.02.010

Disturbance pattern recognition method of power quality based on the fuzzy clustering analysis
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                        10.19781/j.issn.1673-9140.2022.02.010
                    
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                        CHEN XiangqunCHEN Xiangqun
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
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YANG MaotaoYANG Maotao
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
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LIU MouhaiLIU Mouhai
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
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HUANG RuiHUANG Rui
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
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YU MinqiYU Minqi
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
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WANG ZhiWANG Zhi
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
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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.

Key words:power quality disturbance;HHT;fuzzy clustering;pattern recognition;voltage mutation

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陈向群,杨茂涛,刘谋海,黄瑞,余敏琪,王智.基于模糊聚类分析的电能质量扰动模式识别方法[J].电力科学与技术学报英文版,2022,37(2):79-85. CHEN Xiangqun, YANG Maotao, LIU Mouhai, HUANG Rui, YU Minqi, WANG Zhi. Disturbance pattern recognition method of power quality based on the fuzzy clustering analysis[J]. Journal of Electric Power Science and Technology,2022,37(2):79-85.

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Online: May 26,2022
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