Study on Multi-RBF-SVM for Transformer Fault Diagnosis
- Resource Type
- Conference
- Authors
- Qu, Li-ping; Zhou, Hao-han; Liu, Chong-jie; Lu, Zhao
- Source
- 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) DCABES Distributed Computing and Applications for Business Engineering and Science (DCABES), 2018 17th International Symposium on. :188-191 Oct, 2018
- Subject
- Computing and Processing
Oil insulation
Fault diagnosis
Kernel
Support vector machines
Power transformer insulation
Prediction algorithms
fault diagnosis
new three-ratio
M RBF SVM
Kernel function
accuracy
- Language
- ISSN
- 2473-3636
Two improved fault diagnosis algorithms were proposed in this paper. One is New Three-Ratio (NTR) algorithm which introduces the code "000"as normal type code into the Transformer Three-Ratio(TTR). The other is Multi-Radial Basis Function-Support Vector Machine (M-RBF-SVM) algorithm which introduces the multi-RBF kernel function. And, the representative Tradition Three-Ratio algorithm is selected as the simulation comparison object. The results indicate that M-RBF-SVM can achieve higher diagnosis accuracy and excellently generalization ability.