Research of Circuit Breaker Fault Recognition Method Based on Adaptive Weighted of Evidence Theory

被引：0

作者：

Zhao S. ^{[1
]}

Wang Y. ^{[1
]}

Sun H. ^{[1
]}

Wei Y. ^{[1
]}

机构：

[1] School of Electrical and Electronic Engineering, North China Electric Power University, Baoding, 071003, Hebei Province

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2017年 / 37卷 / 23期

关键词：

Adaptive weight; Evidence theory; Fault recognition; SVM;

D O I：

10.13334/j.0258-8013.pcsee.161394

中图分类号：

学科分类号：

摘要：

The circuit breaker operates with a sharp vibration and sound signal. In this paper, a circuit breaker mechanical fault method based on adaptive weight was proposed. Firstly, wavelet packet was used to decompose the vibration and sound signal of multi-sensor. Then the characteristic entropy could be extracted and put into LIBSVM(A Library for Support Vector Machines) to obtain the basic probability assignment. The status classification accuracy was applied to the weight adaptive assignment. Finally, the basic reliability of multi-signal weighting was integrated by using D-S (Dempster Shafer) evidence theory. Different status including normal, sticking, pedestal loosing and break rejecting could be recognized. According to the identification experiments, the evidence theory of adaptive weight can effectively improve the accuracy of fault type diagnosis of the circuit breaker in the case of evidence conflict avoidance. © 2017 Chin. Soc. for Elec. Eng.

引用

页码：7040 / 7046

页数：6

共 21 条

[1]

Sun L., Hu X., Ji Y., Mechanical fault classification of high voltage circuit breakers based on support vector machine, Transactions of China Electrometrical Society, 21, 8, pp. 53-58, (2006)

[2]

Wu Z., Wailg W., Huang M., Et al., Analysis of characteristics of vibration signals in high voltage circuit breakers based on phase space reconstruction, Modem Electric Power, 23, 1, pp. 10-14, (2006)

[3]

Chen W., Deng B., Yang B., Fault recognition for high voltage circuit breaker based on EMD of vibration signal and energy entropy characteristic, High-voltage Apparatus, 45, 2, pp. 90-93, (2009)

[4]

Zhao S., Zhang P., Shen L., Et al., High voltage circuit breaker fault diagnosis by sound and vibrations, Transactions of China Electrometrical Society, 29, 7, pp. 216-221, (2014)

[5]

Qi H., Zhao Z., Li Z., Et al., Vacuum breaker fault diagnosis using multisensory vibration signal fusion, High Voltage Apparatus, 49, 2, pp. 43-48, (2013)

[6]

Ding S., Qi B., Tan H., Theories and algorithms of support vector machine, Journal of University of Electronic Science and Technology of China, 40, 1, pp. 2-10, (2011)

[7]

Guo C., Zhu C., Zhang L., Et al., The method of multi-core multi-classification support vector machine in transformer fault diagnosis, Proceedings of the CSEE, 30, 13, pp. 128-134, (2010)

[8]

Zhang X., On statistical learning theory and support vector machine, Acta Automatica Sinica, 26, 1, pp. 32-42, (2000)

[9]

Zhang P., Zhou X., High voltage circuit breaker mechanical fault diagnosis method based on three-dimensional map sound vibration and time, High Voltage Apparatus, 12, pp. 121-125, (2014)

[10]

He Z., Chen X., Multiscale energy statistics and wavelet energy entropy in power system transient signals identification method, Proceedings of the CSEE, 26, 10, pp. 33-39, (2006)

← 1 2 3 →