CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance
In recent years, deep belief network (DBN) based transformer fault diagnosis methods have been developed. However, they share two prominent drawbacks, which are the low accuracy issue caused by the within-class imbalance of transformer faults samples and the artificial determination of the network p...
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| Format: | Article |
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State Grid Energy Research Institute
2023-08-01
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| Series: | Zhongguo dianli |
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| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202305039 |
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| author | Shuang WANG Qian LUO Bo TANG Lan JIANG Jin LI |
| author_facet | Shuang WANG Qian LUO Bo TANG Lan JIANG Jin LI |
| author_sort | Shuang WANG |
| collection | DOAJ |
| description | In recent years, deep belief network (DBN) based transformer fault diagnosis methods have been developed. However, they share two prominent drawbacks, which are the low accuracy issue caused by the within-class imbalance of transformer faults samples and the artificial determination of the network parameters of deep belief network (DBN). In this paper, a transformer fault diagnosis method based on sample balance processing and improved DBN is proposed. Firstly, an improved K-means (IK-means) synthesis minority oversampling technique (SMOTE) algorithm is proposed to obtain within-class and between-class balanced fault samples. Then, the Tent chaotic map embedded chaotic hybrid pelican optimization algorithm (CHPOA) is developed to optimize the number of hidden layer nodes and reverse fine-tuning learning rate of DBN, and the CHPOA-DBN transformer fault diagnosis model is constructed. Finally, the classical oversampling algorithm, the classical fault diagnosis model and the proposed method are compared and analyzed, based on the experimental data, respectively. The results show that the fault diagnosis accuracy of the proposed method reaches 96.25 %, which provide an important reference for intelligent fault diagnosis under imbalanced fault samples of transformers. |
| format | Article |
| id | doaj-art-9573b40d147e485cb20e4f60501ea924 |
| institution | DOAJ |
| issn | 1004-9649 |
| language | zho |
| publishDate | 2023-08-01 |
| publisher | State Grid Energy Research Institute |
| record_format | Article |
| series | Zhongguo dianli |
| spelling | doaj-art-9573b40d147e485cb20e4f60501ea9242025-08-20T02:47:35ZzhoState Grid Energy Research InstituteZhongguo dianli1004-96492023-08-01561013314410.11930/j.issn.1004-9649.202305039zgdl-56-09-wangshuangCHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class ImbalanceShuang WANG0Qian LUO1Bo TANG2Lan JIANG3Jin LI4School of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaSchool of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaSchool of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaSchool of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaState Grid Hubei Extra High Voltage Company, Wuhan 430051, ChinaIn recent years, deep belief network (DBN) based transformer fault diagnosis methods have been developed. However, they share two prominent drawbacks, which are the low accuracy issue caused by the within-class imbalance of transformer faults samples and the artificial determination of the network parameters of deep belief network (DBN). In this paper, a transformer fault diagnosis method based on sample balance processing and improved DBN is proposed. Firstly, an improved K-means (IK-means) synthesis minority oversampling technique (SMOTE) algorithm is proposed to obtain within-class and between-class balanced fault samples. Then, the Tent chaotic map embedded chaotic hybrid pelican optimization algorithm (CHPOA) is developed to optimize the number of hidden layer nodes and reverse fine-tuning learning rate of DBN, and the CHPOA-DBN transformer fault diagnosis model is constructed. Finally, the classical oversampling algorithm, the classical fault diagnosis model and the proposed method are compared and analyzed, based on the experimental data, respectively. The results show that the fault diagnosis accuracy of the proposed method reaches 96.25 %, which provide an important reference for intelligent fault diagnosis under imbalanced fault samples of transformers.https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202305039transformer fault diagnosiswithin-class imbalancesample balance processingtent chaotic mapdbn network parameters optimization |
| spellingShingle | Shuang WANG Qian LUO Bo TANG Lan JIANG Jin LI CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance Zhongguo dianli transformer fault diagnosis within-class imbalance sample balance processing tent chaotic map dbn network parameters optimization |
| title | CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance |
| title_full | CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance |
| title_fullStr | CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance |
| title_full_unstemmed | CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance |
| title_short | CHPOA-DBN Transformer Fault Diagnosis Method Considering Sample Within-Class Imbalance |
| title_sort | chpoa dbn transformer fault diagnosis method considering sample within class imbalance |
| topic | transformer fault diagnosis within-class imbalance sample balance processing tent chaotic map dbn network parameters optimization |
| url | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202305039 |
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