Transformer Fault Prediction Based on Particle Swarm Optimization and SVM

摘要：

Forecasting of dissolved gases content in power transformer oil is very significant to detect incipient failures of transformer early and ensure normal operation of entire power system. Forecasting of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve regression problem of nonlinearity and small sample. However, it is different to choice the best parameters of the SVM, In this study, support vector machine is proposed to forecast dissolved gases content in power transformer oil, among which Panicle Swarm Optimization (PSO) are used to determine free parameters of support vector machine. The experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed PSO-SVM model. The experimental results indicate that the proposed PSO-SVM model can achieve greater forecasting accuracy than grey model (GM) under the circumstances of small sample. Consequently, the PSO-SVM model is a proper alternative for forecasting dissolved gases content in power transformer oil.

关键词： support vector machine article warm optimization regression algorithm free parameters fault prediction

作者: Yan Zhang Bide Zhang ZichunPei YanWang

作者单位: Institute of Electrical and Information Xihua University Chengdu,China (610039)

会议类型: 国际会议

会议名称: Third International Conference on Digital Image Processing(ICDIP 2011)(第三届数字图像处理国际会议)

会议地点: 成都

会议语种:英文

页码: 154-158

在线出版日期: 2011-04-15（万方平台首次上网日期，不代表论文的发表时间）