Title :
Fault diagnosis model of power transformer based on an improved binary tree and the choice of the optimum parameters of multi-class SVM
Author :
Sun, Xiaoyun ; An, Guoqing ; Fu, Ping ; Bian, Jianpeng
Author_Institution :
Sch. of Electr. Eng. & Inf. Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
Abstract :
An improved binary tree algorithm is proposed for the practical problem of the relativity position of the data sets for oil-immersed transformer in the pattern feature space. And a fault diagnosis model of dissolved gas analysis (DGA) based on an improved binary tree multi-class support vector machine (SVM) is constructed. This method overcomes the disadvantage that the traditional binary tree, which doesn´t consider the distributing situation of the data sets, constructs directly the SVM classifier. At the same time, the two-divided method presented by the paper is applied in the choice of the optimal parameters of SVM. The experiment is performed and this method acquires a better performance.
Keywords :
fault diagnosis; pattern classification; power engineering computing; power transformers; support vector machines; transformer oil; trees (mathematics); binary tree algorithm; dissolved gas analysis; multiclass SVM classifier; multiclass support vector machine; oil immersed transformer; pattern feature space; power transformer fault diagnosis; Binary trees; Classification tree analysis; Dissolved gas analysis; Fault diagnosis; Oil insulation; Power system modeling; Power transformers; Space technology; Support vector machine classification; Support vector machines; Fault diagnosis; improved SVM binary tree; two-divided;
Conference_Titel :
Intelligent Computing and Intelligent Systems, 2009. ICIS 2009. IEEE International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-4754-1
Electronic_ISBN :
978-1-4244-4738-1
DOI :
10.1109/ICICISYS.2009.5357614
