Fault Diagnosis of Transformer Based on Random Forest

摘要：

Fault diagnosis of transformer in power system is studied in this paper. Considering the excellent performances of Random Forest (RF) in pattern recognition, we apply RF to construct a diagnosis model to predict the situation of transformer. The experiments of fault diagnosis for some real transformers show that RF obtains a better result in prediction accuracy and stability than traditional Back Propagation neural network does. In addition, the order of influence factors given by RF is helpful in fault diagnosis.

关键词： Rondom Forest fault diagnosis of transformer classification model

作者: Xi Chen Hongmei Cui Linkai Luo

作者单位: Department of Automation, Xiamen University,Xiamen, P.R China Jiyuan Power Supply Company, Henan Electric Power Company, Jiyuan, P.R China

会议类型: 国际会议

会议名称: 2011 Fourth International Conference on Intelligent Computation Technology and Automation(2011年第四届智能计算技术与自动化国际会议 ICICTA 2011)

会议地点: 深圳

会议语种:英文

页码: 132-134

在线出版日期: 2011-03-28（万方平台首次上网日期，不代表论文的发表时间）