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《Proceedings of the Csee》 2003-07
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DONG Ming, MENG Yuan-yuan, XU Chang-xiang, YAN Zhang (Xian Jiaotong University, Xian 710049, China)  
A multi-level decision-making model for power transformer fault diagnosis based on SVM (Support Vector Machine) is presented. Based on correlation analysis, some key gases are selected as the inputs of SVM; furthermore, improving the use of the fault information within DGA (Dissolved Gas Analysis), the fault diagnosis is accomplished according to the concentration distribution of typical fault gases in higher dimensional space. The proposed approach is based on seeking the optimal solution by few training samples supporting, and it has important features such as good generalization and consistency performance, etc, which is very suitable to solve the problems of less typical fault data for diagnosis. And the comparison between two kinds of SVM is presented also. Meanwhile the output of this model is improved by approaching exactly with K-Nearest Neighbor Search Classification for the SVM classification results, which is adjacent to optimal separating hyperplane. So the dependability of this model is enhanced greatly, and its effectiveness and usefulness is proved.
【Fund】: 国家自然科学基金项目(59637200);; 东北电力集团项目。~~
【CateGory Index】: TM407
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