Research on Power Transformer Fault Diagnosis Based on Decision Tree Support Vector Machine Algorithm

doi:10.11985/2019.04.006

Abstract

Abstract:

Support vector machine algorithms and decision tree algorithms are widely used in power transformer fault diagnosis. Compared with other classification algorithms, the generalization performance of support vector machines is stronger, but the classification efficiency is lower. The decision tree algorithm has higher classification efficiency. A power transformer fault diagnosis model based on classification decision tree is proposed. The sample points close to the decision boundary are classified by the support vector machine, and the remaining samples are classified by the decision tree. The example proves that the method has high classification accuracy.

Key words: Power transformer, fault diagnosis, decision trees, multiclass classification, support vector machine

CLC Number:

TM407

Zhipeng SUN,Zhilong SUN,Jian WEI. Research on Power Transformer Fault Diagnosis Based on Decision Tree Support Vector Machine Algorithm[J]. Journal of Electrical Engineering, 2019, 14(4): 42-45.

Figures/Tables 4

References 13

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序号	①V②	②V③	③V④	①V④
1	0.901	-0.347	-0.623	0.810
2	0.865	-0.500	0.871	-0.755
3	-1.000	0.672	0.439	-0.765
4	-0.985	0.665	-0.751	-0.500
5	-0.800	0.578	0.768	-0.233
6	0.780	-0.662	0.500	-0.855
7	1.000	0.450	-0.872	0.766
8	0.676	-0.671	-0.662	-0.865

序号	故障1	故障2	故障3	故障4	结果
1	2	0	1	1	高能放电
2	1	0	2	1	局部放电
3	0	2	1	1	低能放电
4	0	1	0	2	高温过热
5	0	2	1	1	低能放电
6	1	0	2	1	局部放电
7	2	1	0	1	高能放电
8	1	0	1	2	高温过热

编号	H₂	CH₄	C₂H₆	C₂H₄	C₂H₂	实际故障
1	100	94	22	304	118	高能放电
2	346	58	54	14	0	局部放电
3	46.7	8.2	1.8	5.1	4.1	低能放电
4	90	160	54	330	29	高温过热
5	49.1	12.2	0.3	3.9	4.8	低能放电
6	230	106	42	17	0	局部放电
7	250	63	2.8	66	120	高能放电
8	181	316	115	593	2	高温过热

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