• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 19 Issue 2
Apr.  2006
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Article Contents
ZHOU Lijun, WU Guangning, SU Chong, WANG Hongliang. Compound Approach of Predicting Fault Gases Dissolved in Transformer Oil[J]. Journal of Southwest Jiaotong University, 2006, 19(2): 150-153.
Citation: ZHOU Lijun, WU Guangning, SU Chong, WANG Hongliang. Compound Approach of Predicting Fault Gases Dissolved in Transformer Oil[J]. Journal of Southwest Jiaotong University, 2006, 19(2): 150-153.

Compound Approach of Predicting Fault Gases Dissolved in Transformer Oil

  • Received Date: 21 Mar 2005
  • Publish Date: 25 Apr 2006
  • To improve the prediction result for transformer faults,a compound approach combining GM (1,1) model with self-learning BP-neural networks was proposed to predict fault gases dissolved in transformer oil.In this approach,the concentration and development trend of gases dissolved in transformer oil are predicted primarily using GM (1,1) model,and then the predicted results are calibrated by self-learning BP-neural networks with calibrated parameters obtained by analyzing the interaction of different types of gases and the relationship between the time sequences of gas concentrations.The proposed approach has been used in the practice of transformer fault prediction to show its validity.

     

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