Neural Network for the Prediction of Transformer Oil Performance with Multi- parameter Correlation

摘要：

Based on the fact that performance parameters of transformer oil do correlate, this paper established a prediction method of breakdown voltage which is the most important parameter of transformer oil via multi-parameter correlation under the development environment of Vlatlab. 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 some relational models of breakdown voltage and those parameters were further constructed using traditional BP neural network, improved BP neural network and RBF neural network. The clustering centers used to train network were acquired through clustering the original monitoring data samples with fuzzy C-means clustering algorithm. 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 prediction performance of the RBF network and improved BP network is higher and that the relative errors between predicted values and real values are all less than 10％, which can meet the requirements of practical application and indicates the significant practical value of these models.

关键词： breakdown voltage grey theory neural network fuzzy clustering prediction

作者: Jia-yuan Hu Shun-an Cao Zhi Li Jian-li Xie

作者单位: College of Power and Mechanical Engineering, Wuhan University Hubei Wuhan, China Electric Power Research Institute of Guangdong Power Grid Corporation Guangdong Guangzhou, China College of Chemistry and Molecular Sciences, Wuhan University Hubei Wuhan, China

会议类型: 国际会议

会议名称: 2010 International Conference on Measurement and Control Engineering(2010年IEEE测量与控制工程国际会议 ICMCE2010)

会议地点: 成都

会议语种:英文

页码: 89-93

在线出版日期: 2010-11-16（万方平台首次上网日期，不代表论文的发表时间）