Earticle

초록

영어

DGA (Dissolved Gas Analysis) is the traditional transformer fault diagnosis method, but it mainly depends on the experience of operators. In order to solve the limitations of traditional method, this paper introduces intelligent method for fault diagnosis of transformer. The intelligent method made fusion of various data, including SCADA data, oil dissolved gas sensor data, related electrical test data, operation maintenance records, and so on, employed space-time weighting fusion method based on BP neural network, and put forward the model of transformer fault diagnosis based on multi-source information fusion, which improved the accuracy of the transformer fault diagnosis dramatically.

목차

Abstract
 1. Introduction
 2. Related Works
  2.1. Multi-source information fusion model
  2.2. Transformer fault diagnosis method
 3. Transformer Fault Diagnosis Based on Multi-source Information Fusion
  3.1. Model of transformer fault diagnosis based on Multi-source information fusion
  3.2 Space-time fusion of multi-sensor based on BP neural network
  3.3 Procedure of transformer fault diagnosis based on BP neural network
 4. Comparison of Transformer Fault Diagnosis Result
 5. Conclusion
 References

키워드

저자

• Xiaohui Wang [ Postdoctoral Mobile Research Station of Management Science and Engineering, North China Electric Power University, Beijing 102206 P. R. China ]
• Kehe Wu [ School of Control and Computer Engineering, North China Electric Power University, Beijing 102206 P. R. China ]
• Yang Xu [ School of Control and Computer Engineering, North China Electric Power University, Beijing 102206 P. R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605