Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM
Power transformers are the vital and expensive components of the power system. Timely identifying and diagnosing the transformer faults is critical to maintaining the stability of the power grid. As a sensitive and economical tool, the frequency response analysis (FRA) method has been widely employe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10491192/ |
| _version_ | 1827287503864856576 |
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| author | Guohao Wang Shengxuan Qiu Fei Xie Tengqi Luo Ying Song Song Wang |
| author_facet | Guohao Wang Shengxuan Qiu Fei Xie Tengqi Luo Ying Song Song Wang |
| author_sort | Guohao Wang |
| collection | DOAJ |
| description | Power transformers are the vital and expensive components of the power system. Timely identifying and diagnosing the transformer faults is critical to maintaining the stability of the power grid. As a sensitive and economical tool, the frequency response analysis (FRA) method has been widely employed to detect winding faults. However, it is still a challenge to accurately identify the fault types and degrees only by the FRA method. In this article, a new diagnosis method that combines the FRA method with a kernel-based extreme learning machine (KELM) optimized by a seagull optimization algorithm (SOA), is proposed to diagnose the fault types and degrees of the winding. First, a series of FRA tests are performed on a laboratory winding model under three different faults to obtain the FRA dataset. Furthermore, various numerical indices are applied to extract the characteristics of FRA signatures to train the SOA-KELM model. Then, the trained SOA-KELM model is utilized to classify fault types and degrees of the winding. Finally, the feasibility and superiority of SOA-KELM are verified by comparing with SOA optimized support vector machine (SOA-SVM) and random forest (SOA-RF), particle swarm optimization (PSO) algorithm optimized KELM (PSO-KELM), PSO-SVM, PSO-RF, SVM, RF, and KELM from the aspects of diagnosis accuracy and running time. The comprehensive comparison results show that SOA-KELM has the best diagnosis performance. |
| first_indexed | 2024-04-24T11:00:34Z |
| format | Article |
| id | doaj.art-3b948bc9014741c8a19fc21eb3eb54e0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T11:00:34Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-3b948bc9014741c8a19fc21eb3eb54e02024-04-11T23:00:21ZengIEEEIEEE Access2169-35362024-01-0112502875029910.1109/ACCESS.2024.338522910491192Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELMGuohao Wang0https://orcid.org/0009-0001-1982-6668Shengxuan Qiu1Fei Xie2Tengqi Luo3Ying Song4Song Wang5https://orcid.org/0000-0003-2551-1360Department of Power and Electrical Engineering, Northwest A&F University, Yangling, ChinaDepartment of Power and Electrical Engineering, Northwest A&F University, Yangling, ChinaDepartment of Power and Electrical Engineering, Northwest A&F University, Yangling, ChinaDepartment of Power and Electrical Engineering, Northwest A&F University, Yangling, ChinaDepartment of Power and Electrical Engineering, Northwest A&F University, Yangling, ChinaDepartment of Power and Electrical Engineering, Northwest A&F University, Yangling, ChinaPower transformers are the vital and expensive components of the power system. Timely identifying and diagnosing the transformer faults is critical to maintaining the stability of the power grid. As a sensitive and economical tool, the frequency response analysis (FRA) method has been widely employed to detect winding faults. However, it is still a challenge to accurately identify the fault types and degrees only by the FRA method. In this article, a new diagnosis method that combines the FRA method with a kernel-based extreme learning machine (KELM) optimized by a seagull optimization algorithm (SOA), is proposed to diagnose the fault types and degrees of the winding. First, a series of FRA tests are performed on a laboratory winding model under three different faults to obtain the FRA dataset. Furthermore, various numerical indices are applied to extract the characteristics of FRA signatures to train the SOA-KELM model. Then, the trained SOA-KELM model is utilized to classify fault types and degrees of the winding. Finally, the feasibility and superiority of SOA-KELM are verified by comparing with SOA optimized support vector machine (SOA-SVM) and random forest (SOA-RF), particle swarm optimization (PSO) algorithm optimized KELM (PSO-KELM), PSO-SVM, PSO-RF, SVM, RF, and KELM from the aspects of diagnosis accuracy and running time. The comprehensive comparison results show that SOA-KELM has the best diagnosis performance.https://ieeexplore.ieee.org/document/10491192/Power transformerfrequency response analysis (FRA)kernel extreme learning machine (KELM)seagull optimization algorithm (SOA) |
| spellingShingle | Guohao Wang Shengxuan Qiu Fei Xie Tengqi Luo Ying Song Song Wang Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM IEEE Access Power transformer frequency response analysis (FRA) kernel extreme learning machine (KELM) seagull optimization algorithm (SOA) |
| title | Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM |
| title_full | Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM |
| title_fullStr | Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM |
| title_full_unstemmed | Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM |
| title_short | Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM |
| title_sort | diagnosing fault types and degrees of transformer winding combining fra method with soa kelm |
| topic | Power transformer frequency response analysis (FRA) kernel extreme learning machine (KELM) seagull optimization algorithm (SOA) |
| url | https://ieeexplore.ieee.org/document/10491192/ |
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