On-line fault diagnosis method for power transformer based on missing data repair

被引：0
|
作者：
Chen J. [1 ]
Yang X. [1 ]
Ye C. [2 ]
Tang J. [1 ]
Li X. [1 ]
Fang X. [1 ]
Long H. [2 ]
机构：
[1] Hangzhou Power Supply Company of State Grid Electric Power Corporation Ltd., Hangzhou
[2] College of Electrical Engineering, Zhejiang University, Hangzhou
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2019年 / 47卷 / 15期
基金：
中国国家自然科学基金;
关键词：
Fault diagnosis; K-dimensional tree; K-nearest neighbor; Support vector machine; Transformer;
D O I：
10.19783/j.cnki.pspc.181024
中图分类号：
学科分类号：
摘要：
Data quality is an important factor affecting the accuracy of transformer fault diagnosis. In order to solve the problem of missing on-line monitoring data for transformer dissolved gas, an on-line fault diagnosis method using loop iterations of improved k-nearest neighbors and multi-class SVMs for power transformer based on missing data repair is proposed. In the k-nearest neighbor method, the Manhattan distance which is weighted by the negative exponent of the correlation coefficient is used to measure the distance between samples. On one hand, the influence of the strong correlation indexes on the missing data can be highlighted to improve the accuracy of data repair. On the other hand, the improved Manhattan distance is suitable for an efficient search strategy based on k-d tree, which can achieve fast search for massive historical data and meet the real-time demand of on-line diagnosis. Diagnosis test results show that the proposed method can reduce the influence of missing data on the accuracy of transformer fault diagnosis and realize the accurate and efficient on-line diagnosis for transformer fault. © 2019, Power System Protection and Control Press. All right reserved.
引用
收藏
页码：86 / 92
页数：6
相关论文
共 23 条
  • [1] Huang J., Ge S., Han J., Et al., A diagnostic method for distribution networks based on power supply safety standards, Protection and Control of Modern Power Systems, 1, 1, pp. 63-70, (2016)
  • [2] Lu S., Cui Y., Feng C., Et al., Transformer fault diagnosis method based on comprehensive analysis and its software development, Power System Protection and Control, 45, 10, pp. 123-128, (2017)
  • [3] Zhang D., Xu Y., Wang Y., Neural network ensemble method and its application in DGA fault diagnosis of power transformer on the basis of active diverse learning, Proceedings of the CSEE, 30, 22, pp. 64-70, (2010)
  • [4] Zheng H.B., Liao R.J., Grzybowski S., Et al., Fault diagnosis of power transformers using multi-class least square support vector machines classifiers with particle swarm optimization, IET Electric Power Application, 5, 9, pp. 691-696, (2011)
  • [5] Yang X., Wang Y., Rao D., Et al., Design and application of IED in integrated monitoring intelligent component of transformer, Power System Protection and Control, 45, 16, pp. 130-135, (2017)
  • [6] Liu Y., Luan W., Xu Y., Et al., Data cleaning method for distribution transformer, Power System Technology, 41, 3, pp. 1008-1014, (2017)
  • [7] Liu N., Gao W., Tan K., Fault diagnosis of power transformer using a combinatorial neural network, Transactions of China Electrotechnical Society, 18, 12, pp. 11-16, (2003)
  • [8] Li Z., Qian L., Li J., Type recognition of partial discharge in power transformer based on statistical characteristics and PNN, Power System Protection and Control, 46, 13, pp. 55-60, (2018)
  • [9] Zang H., Hu Y., Yu X., Integrated ANN based on radial basis function applied in transformer fault diagnosis, Proceedings of the CSU-EPSA, 15, 1, pp. 51-53, (2003)
  • [10] Wu L., Zhu Y., Yuan J., Novel method for transformer fault integrated diagnosis based on Bayesian network classifier, Transactions of China Electortechnical Society, 20, 4, pp. 45-51, (2005)
123