The Multi-class SVM is Applied in Transformer Fault Diagnosis

摘要：

　　Transformer fault forecast plays an important role in the safe and stable operation of power system.So it is important to detect the incipient faults of transformer as early as possible.In this study,the support vector machine(SVM)is introduced to analyze and diagnosis the transformer fault.According to the accumulation fault data,the SVM forecast model take the RBF as the kernel function and utilize the best pattern to cope with data for reducing imbalance.In order to prove the SVM method efficacious and accuracy,we also make the diagnosis with traditional three ratio method experimental.The results of the final experimental indicate that SVM can make higher diagnosis accuracy and have excellently generalization ability.

关键词： Support Vector Machine Transformer Fault Fault Diagnosis Experimental

作者: Liping Qu Haohan Zhou

作者单位: Electric & Information Engineering School Beihua University Jilin,China

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 477-480

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）