Transformer Hierarchical Fault Diagnosis Model Based on Dissolved Gas Analysis of Insulating Oil and Class Overlap Features
Dissolved gas analysis (DGA) of insulating oil can effectively identify transformer discharge fault and overheating fault. In order to improve the accuracy of transformer fault diagnosis, a transformer hierarchical fault diagnosis method is proposed based on class overlap features. Firstly, the supp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2022-07-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202202010 |
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| Summary: | Dissolved gas analysis (DGA) of insulating oil can effectively identify transformer discharge fault and overheating fault. In order to improve the accuracy of transformer fault diagnosis, a transformer hierarchical fault diagnosis method is proposed based on class overlap features. Firstly, the support vector data description (SVDD) is used to divide the overlapping region of transformer fault sample data spaces, and the class overlap rate and class overlap degree are selected as the overlapping features to describe the class overlap degree and the importance of sample points respectively. And then, a hierarchical fault diagnosis model is established based on the class overlap rate. The samples of each diagnosis layer are trained separately by the separate training method, and a two-class fuzzy support vector machine (FSVM) is constructed based on class overlap degrees to diagnose faults. Experimental results show that the proposed method is more accurate than other models. |
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| ISSN: | 1004-9649 |