A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit
The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Th...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/3/672 |
| _version_ | 1797624733393485824 |
|---|---|
| author | Wanjie Lu Chun Shi Hua Fu Yaosong Xu |
| author_facet | Wanjie Lu Chun Shi Hua Fu Yaosong Xu |
| author_sort | Wanjie Lu |
| collection | DOAJ |
| description | The bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO is further proposed to optimize the BiGRU parameters to build an optimal diagnosis model based on BiGRU. For the ISCSO, four improvement methods are proposed. The traditional sand cat swarm algorithm is improved using logistic chaotic mapping, the water wave dynamic factor, adaptive weighting, and the golden sine strategy. Then, benchmarking functions are used to test the optimization performance of ISCSO and the four algorithms, and the results show that ISCSO has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on L-Isomap and ISCSO-BiGRU is obtained. Using the model for fault diagnosis, the example simulation results show that using L-ISOMP to filter and downscale the model inputs can better improve model performance. The results are compared with the SCSO-BiGRU, WOA-BiGRU, GWO-BiGRU, and PSO-BiGRU fault diagnosis models. The results show that the fault diagnosis rate of ISCSO-BiGRU is 94.8%, which is 11.69%, 10.39%, 7.14%, and 5.9% higher than that of PSO-BiGRU, GWO-BiGRU, WOA-BiGRU, and SCSO-BiGRU, respectively, and validate that the proposed method can effectively improve the fault diagnosis performance of transformers. |
| first_indexed | 2024-03-11T09:47:27Z |
| format | Article |
| id | doaj.art-27380c2839884a809193eea182b61f12 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T09:47:27Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-27380c2839884a809193eea182b61f122023-11-16T16:29:49ZengMDPI AGElectronics2079-92922023-01-0112367210.3390/electronics12030672A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent UnitWanjie Lu0Chun Shi1Hua Fu2Yaosong Xu3School of Electrical Control, Liaoning Technical University, Huludao 125000, ChinaSchool of Electrical Control, Liaoning Technical University, Huludao 125000, ChinaSchool of Electrical Control, Liaoning Technical University, Huludao 125000, ChinaSchool of Electrical Control, Liaoning Technical University, Huludao 125000, ChinaThe bidirectional gated recurrent unit (BiGRU) method based on dissolved gas analysis (DGA) has been studied in the field of power transformer fault diagnosis. However, there are still some shortcomings such as the fuzzy boundaries of DGA data, and the BiGRU parameters are difficult to determine. Therefore, this paper proposes a power transformer fault diagnosis method based on landmark isometric mapping (L-Isomap) and Improved Sand Cat Swarm Optimization (ISCSO) to optimize the BiGRU (ISCSO-BiGRU). Firstly, L-Isomap is used to extract features from DGA feature quantities. In addition, ISCSO is further proposed to optimize the BiGRU parameters to build an optimal diagnosis model based on BiGRU. For the ISCSO, four improvement methods are proposed. The traditional sand cat swarm algorithm is improved using logistic chaotic mapping, the water wave dynamic factor, adaptive weighting, and the golden sine strategy. Then, benchmarking functions are used to test the optimization performance of ISCSO and the four algorithms, and the results show that ISCSO has the best optimization accuracy and convergence speed. Finally, the fault diagnosis method based on L-Isomap and ISCSO-BiGRU is obtained. Using the model for fault diagnosis, the example simulation results show that using L-ISOMP to filter and downscale the model inputs can better improve model performance. The results are compared with the SCSO-BiGRU, WOA-BiGRU, GWO-BiGRU, and PSO-BiGRU fault diagnosis models. The results show that the fault diagnosis rate of ISCSO-BiGRU is 94.8%, which is 11.69%, 10.39%, 7.14%, and 5.9% higher than that of PSO-BiGRU, GWO-BiGRU, WOA-BiGRU, and SCSO-BiGRU, respectively, and validate that the proposed method can effectively improve the fault diagnosis performance of transformers.https://www.mdpi.com/2079-9292/12/3/672power transformerfault diagnosislandmark isometric mappingbidirectional gated recurrent unitimproved sand cat swarm optimization algorithm |
| spellingShingle | Wanjie Lu Chun Shi Hua Fu Yaosong Xu A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit Electronics power transformer fault diagnosis landmark isometric mapping bidirectional gated recurrent unit improved sand cat swarm optimization algorithm |
| title | A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit |
| title_full | A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit |
| title_fullStr | A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit |
| title_full_unstemmed | A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit |
| title_short | A Power Transformer Fault Diagnosis Method Based on Improved Sand Cat Swarm Optimization Algorithm and Bidirectional Gated Recurrent Unit |
| title_sort | power transformer fault diagnosis method based on improved sand cat swarm optimization algorithm and bidirectional gated recurrent unit |
| topic | power transformer fault diagnosis landmark isometric mapping bidirectional gated recurrent unit improved sand cat swarm optimization algorithm |
| url | https://www.mdpi.com/2079-9292/12/3/672 |
| work_keys_str_mv | AT wanjielu apowertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT chunshi apowertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT huafu apowertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT yaosongxu apowertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT wanjielu powertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT chunshi powertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT huafu powertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit AT yaosongxu powertransformerfaultdiagnosismethodbasedonimprovedsandcatswarmoptimizationalgorithmandbidirectionalgatedrecurrentunit |