Iterative Online Fault Identification Scheme for High-Voltage Circuit Breaker Utilizing a Lost Data Repair Technique

Gang Zhou, Zhongjie Han, Jin Fu, Guan Hua Xu and Chengjin Ye
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Gang Zhou: State Grid Zhejiang Jiaxing Power Supply Company, Jiaxing 314100, China
Zhongjie Han: State Grid Zhejiang Jiaxing Power Supply Company, Jiaxing 314100, China
Jin Fu: State Grid Zhejiang Jiaxing Power Supply Company, Jiaxing 314100, China
Guan Hua Xu: College of Electric Engineering, Zhejiang University, Hangzhou 310027, China
Chengjin Ye: College of Electric Engineering, Zhejiang University, Hangzhou 310027, China

Energies, 2020, vol. 13, issue 13, 1-15

Abstract: Most of the prior-art electrical noninvasive monitoring systems adopt Zigbee, Bluetooth, or other wireless communication infrastructure. These low-cost channels are often interrupted by strong electromagnetic interference and result in monitoring anomalies, particularly packet loss, which severely affects the precision of equipment fault identification. In this paper, an iterative online fault identification framework for a high-voltage circuit breaker utilizing a novel lost data repair technique is developed to adapt to low-data quality conditions. Specifically, the improved efficient k-nearest neighbor (kNN) algorithm enabled by a k-dimensional (K-D) tree is utilized to select the reference templates for the unintegrated samples. An extreme learning machine (ELM) is utilized to estimate the missing data based on the selected nearest neighbors. The Softmax classifier is exploited to calculate the probability of the repaired sample being classified to each of the preset status classes. Loop iterations are implemented where the nearest neighbors are updated until their labels are consistent with the estimated labels of the repaired sample based on them. Numerical results obtained from a realistic high-voltage circuit breaker (HVCB) condition monitoring dataset illustrate that the proposed scheme can efficiently identify the operation status of HVCBs by considering measurement anomalies.

Keywords: high-voltage circuit breaker; missing data; extreme learning machine; Softmax; fault diagnosis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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