Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment
Aiming at the problems of coupling between transformer input characteristics and low accuracy of transformer fault diagnosis, SSA-MDS and other soft technologies are used to analyze the key characteristics of transformer faults, so as to improve the accuracy of transformer fault diagnosis. The SSA a...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9869841/ |
| _version_ | 1811273069457571840 |
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| author | Mei Zhang Wanli Chen |
| author_facet | Mei Zhang Wanli Chen |
| author_sort | Mei Zhang |
| collection | DOAJ |
| description | Aiming at the problems of coupling between transformer input characteristics and low accuracy of transformer fault diagnosis, SSA-MDS and other soft technologies are used to analyze the key characteristics of transformer faults, so as to improve the accuracy of transformer fault diagnosis. The SSA algorithm cascade MDS algorithm to process the DGA data is proposed. Subsequently, the TSSA-RF model is introduced to classify the DGA data. The DGA data is first mapped to a high-dimensional space. Next, the optimal feature subset is encoded using the SSA algorithm to reduce irrelevant and redundant features. In this study, the correlation between the optimal feature dimension and the transformer fault diagnosis accuracy is investigated. the expression of the optimal feature subset is obtained by decompiling the SSA operator. The pre-processed data are classified using the RF model, and the TSSA -RF model for classifying the DGA data is found with the highest accuracy through the comparison of different optimization algorithms. After the RF model is optimized using the TSSA algorithm, its accuracy increases by 7.89%, and the accuracy of the TSSA -RF model is obtained as 92.11%. The example results show that compared with the original data, the proposed data processing algorithm improves the diagnostic accuracy of transformer by 11.97 % in the RF model. Compared with multiple preprocessing methods, SSA-MDS has the highest accuracy. Compared with the original data, the accuracy of TSSA-RF model increases by 11.64 %. |
| first_indexed | 2024-04-12T22:52:23Z |
| format | Article |
| id | doaj.art-782e921aeb114227a9ec2be8ebd09fa0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T22:52:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-782e921aeb114227a9ec2be8ebd09fa02022-12-22T03:13:19ZengIEEEIEEE Access2169-35362022-01-0110925059251510.1109/ACCESS.2022.32029829869841Fault Diagnosis of Power Transformer Based on SSA—MDS PretreatmentMei Zhang0https://orcid.org/0000-0003-2013-7613Wanli Chen1https://orcid.org/0000-0002-4507-2177College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, ChinaAiming at the problems of coupling between transformer input characteristics and low accuracy of transformer fault diagnosis, SSA-MDS and other soft technologies are used to analyze the key characteristics of transformer faults, so as to improve the accuracy of transformer fault diagnosis. The SSA algorithm cascade MDS algorithm to process the DGA data is proposed. Subsequently, the TSSA-RF model is introduced to classify the DGA data. The DGA data is first mapped to a high-dimensional space. Next, the optimal feature subset is encoded using the SSA algorithm to reduce irrelevant and redundant features. In this study, the correlation between the optimal feature dimension and the transformer fault diagnosis accuracy is investigated. the expression of the optimal feature subset is obtained by decompiling the SSA operator. The pre-processed data are classified using the RF model, and the TSSA -RF model for classifying the DGA data is found with the highest accuracy through the comparison of different optimization algorithms. After the RF model is optimized using the TSSA algorithm, its accuracy increases by 7.89%, and the accuracy of the TSSA -RF model is obtained as 92.11%. The example results show that compared with the original data, the proposed data processing algorithm improves the diagnostic accuracy of transformer by 11.97 % in the RF model. Compared with multiple preprocessing methods, SSA-MDS has the highest accuracy. Compared with the original data, the accuracy of TSSA-RF model increases by 11.64 %.https://ieeexplore.ieee.org/document/9869841/Power transformerfault diagnosisRF~modelTSSA algorithmfeature extraction |
| spellingShingle | Mei Zhang Wanli Chen Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment IEEE Access Power transformer fault diagnosis RF~model TSSA algorithm feature extraction |
| title | Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment |
| title_full | Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment |
| title_fullStr | Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment |
| title_full_unstemmed | Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment |
| title_short | Fault Diagnosis of Power Transformer Based on SSA—MDS Pretreatment |
| title_sort | fault diagnosis of power transformer based on ssa x2014 mds pretreatment |
| topic | Power transformer fault diagnosis RF~model TSSA algorithm feature extraction |
| url | https://ieeexplore.ieee.org/document/9869841/ |
| work_keys_str_mv | AT meizhang faultdiagnosisofpowertransformerbasedonssax2014mdspretreatment AT wanlichen faultdiagnosisofpowertransformerbasedonssax2014mdspretreatment |