POWER TRANSFORMER FAULT DIAGNOSIS USING SOM-BASED RBF NEURAL NETWORKS

摘要：

A radial basis function (RBF) neural network used in fault diagnosis system is developed for power transformer fault analysis. The Gas extracted from transformer oil is the input of RBF-type neural network architecture. Our proposed cell-splitting grid algorithm determines the optimal network architecture of the RBF network automatically. This facilitates the conventional laborious trail-and-error procedure in establishing an optimal architecture. In this paper, the proposed RBF machine fault diagnostic system has been intensively tested with the overheating faults and discharging faults of power transformer.

关键词： Cell-splitting grid (CSG) radial basis function (RBF) neural network self- organizing map (SOM)

作者: YONG-CHUN LIANG JIAN-YE LIU

作者单位: Department of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050054, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 3140-3143

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）