Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

doi:10.23919/CJEE.2023.000002

Chinese Journal of Electrical Engineering ›› 2023, Vol. 9 ›› Issue (1): 129-141.doi: 10.23919/CJEE.2023.000002

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Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

Hongwei Li¹, Lilong Dou¹, Shuaibing Li^2,*, Yongqiang Kang², Xingzu Yang², Haiying Dong²

1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China;
2. School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Received:2021-04-08 Revised:2021-05-17 Accepted:2021-06-15 Online:2023-03-25 Published:2023-04-06
Contact: *E-mail: lishuaibing1105@163.com
About author:Hongwei Li born in Gansu, China, 1981. He received his bachelor's and master's degree in Lanzhou Jiaotong University in 2003 and 2009 respectively, both majoring in Traffic Information Engineering Control. From 2003 to 2012, he was a Research Engineer with the School of Automation and Electrical Engineering in Lanzhou Jiaotong University, where he was engaged in the research of control of wind generation systems. Since August 2012, he has been studying for his Ph.D. His current research interests are in the areas of coordination control of subsystems of wind turbine.
Lilong Dou was born in Gansu, China, in 1994. He received the B.Sc. degree from Lanzhou Jiaotong University, Lanzhou, China, in 2019. He is currently working toward the M.Sc. degree at Lanzhou Jiaotong University, Lanzhou, China. His research interests include condition monitoring and fault diagnosis of transformer on load tap changer.
Shuaibing Li (S'14-M'19, IEEE) was born in Gansu, China, in 1989. He received the B.Sc. degree in Automation in 2011, and M.Sc. degree in Control Theory and Control Engineering in 2014 from Lanzhou Jiaotong University, and the Ph.D. degree in Electrical Engineering in 2018 from Southwest Jiaotong University. Now, he is a Lecture with the School of New Energy and Power Engineering, Lanzhou Jiaotong University. He is also the PI of the Institute of New Energy Power Systems (INEPS), Lanzhou Jiaotong University. His research interests include information processing, condition monitoring, assessment and fault diagnosis of high-voltage power equipment, and dynamic state estimation of electrical power systems.
Yongqiang Kang (M'17, IEEE) was born in Gansu, China, in 1988. He received the B.Sc. and M.Sc. degree in Control Theory and Control Engineering in Electrical Engineering from Lanzhou Jiaotong University, Lanzhou, China, in 2008 and 2012, respectively, and the Ph.D. degree in Electrical Engineering in 2019 from Southwest Jiaotong University. His research interest includes gas discharge.
Xingzu Yang was born in Hebei,China,in 1996. He graduated from Beijing University of Civil Engineering and Architecture in June 2019 with a B.Sc. degree in Building Electrical and Intelligent major. Currently he is a master degree candidate in the Lanzhou Jiaotong University,under the guidance of Lecturer Li Shuaibing．And presently his main research field is derivation mechanism of contact resistance of oil-immersed on-load tap-changer.
Haiying Dong (M'20, IEEE) was born in Shanxi, China, in 1966. He received the B.S. M.S. and Ph.D. degrees from the Beijing University of Aeronautics and Astronautics, Lanzhou Railway Institute, and Xi'an Jiaotong University, respectively. He is currently a Professor and Ph.D. supervisor with the School of New Energy and Power Engineering, Lanzhou Jiaotong University. Currently, he is the Dean of the School of New Energy and Power Engineering. He has published more than 80 journal papers and been authorized 10 patents. His research interests include condition diagnosis of high-voltage power equipment, optimal operation and intelligent control of power systems, and optimal control of new energy generation.

Abstract

Abstract: An accurate extraction of vibration signal characteristics of an on-load tap changer (OLTC) during contact switching can effectively help detect its abnormal state. Therefore, an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed. First, the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC. Then, the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition (EEMD) is used to decompose the vibration signal and extract the boundary spectrum features. Finally, the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact. An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method. The EEMD can improve the modal aliasing effect, and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts. The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.

Key words: On-load tap changer, singular spectrum analysis, Hilbert-Huang transform, gray wolf optimization algorithm, fuzzy C-means clustering

Hongwei Li, Lilong Dou, Shuaibing Li, Yongqiang Kang, Xingzu Yang, Haiying Dong. Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering[J]. Chinese Journal of Electrical Engineering, 2023, 9(1): 129-141.

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Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

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