NDLI: Prediction Method of Breakdown Voltage of Transformer Oil Based on Fuzzy Clustering Theory and BP Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Jia-yuan Hu Shun-an Cao Zhi Li
Copyright Year	2010
Abstract	Prediction of breakdown voltage of transformer oil facilitates the early fault diagnosis, prevention and treatment of transformer. In this article, a prediction method of breakdown voltage via multi-parameter correlation was proposed considering the lack of research in this field. Through examining the routine monitoring data of transformer oil by gray correlation analysis, some parameters which have strong correlation with breakdown voltage were excavated, then a relational model of breakdown voltage and those parameters was further constructed using back-propagation neural network, since BP neural network has prominent fault-tolerant, non-linear approximation, and self-learning capabilities. The clustering centers which were used to train network were acquired through clustering the original monitoring data samples with fuzzy C-means clustering algorithm. The adoption of this method can resolve natural problems of neural networks caused by large sample capacity, such as complication of net construction, inferior astringency, poor generalization ability, and so on. Test results show that the precision of the prediction model is high and that the relative prediction errors of test samples are all less than 10%, which indicates the significant practical value of the model.
Starting Page	5409
Ending Page	5412
File Size	377412
Page Count	4
File Format	PDF
ISBN	9781424468805
e-ISBN	9781424468812
DOI	10.1109/iCECE.2010.1313
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Breakdown voltage Correlation BP neural network Grey correlation Artificial neural networks Prediction Oil insulation Power transformers Sun Fuzzy C-means clustering
Content Type	Text
Resource Type	Article

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