THE STUDY OF FAULT DIAGNOSIS MODEL OF DGA FOR OIL-IMMERSED TRANSFORMER BASED ON SVM ACTIVE LEARNING AND K-L FEATURE EXTRACTING

摘要：

A model based on Support Vector Machine (SVM) active learning and Karhunen-Loeve (K-L) feature extracting is proposed for oil-immersed transformer fault diagnosis, and a SVM active learning algorithm with the Euclidian distance based on Mercer function is introduced to select the training sample data. The K-L transform is used to extract the characteristics of the sample data set, and the sample data set that has reduced six dimensions to three dimensions is showed in the three-dimensional figure. The SVM active learning algorithm is used to select and classify the fault types. The result shows that the precision is better than the traditional one, and the reliability and effectiveness using above method is Satisfied in fault diagnosis.

关键词： Oil-immersed transformer Fault diagnosis SVM Active learning K-L feature eztracting

作者: XIAO-YUN SUN DONG-HUI LIU JIAN-PENG BIAN

作者单位: School of Electrical Technology & Information Science Hebei University of Science & Technology Shijiazhuang, 050018, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1510-1514

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）