A fault diagnosis method for inter-turn short circuit based on magnetic field distribution
Abstract Inter-turn short circuit (ITSC) faults are among the most critical and frequent failures in power transformer windings. However, conducting a quantitative analysis of the winding insulation state based on MFL remains challenging. This paper proposes a magnetic-electrical spatial state model...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01760-2 |
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| author | Bowen Wang Lulu Wang |
| author_facet | Bowen Wang Lulu Wang |
| author_sort | Bowen Wang |
| collection | DOAJ |
| description | Abstract Inter-turn short circuit (ITSC) faults are among the most critical and frequent failures in power transformer windings. However, conducting a quantitative analysis of the winding insulation state based on MFL remains challenging. This paper proposes a magnetic-electrical spatial state model that links local fault currents to leakage magnetic field variations. A data-driven fault localization framework is developed by combining recursive feature elimination (RFE), Spearman correlation analysis, and support vector machine (SVM) classification. Experimental validation on a 3 kW dry-type transformer, enhanced with FEM-based signal augmentation, shows that the method achieves 97.4% fault localization accuracy under rated load using only 20 Hall-effect sensors. Under no-load conditions, the accuracy remains 92.3%, demonstrating robustness against weak excitation and electromagnetic noise. The optimized sensor layout in the winding gap enhances spatial sensitivity while minimizing hardware complexity. These results confirm the method’s potential for scalable, non-intrusive insulation monitoring in practical power transformers. |
| format | Article |
| id | doaj-art-b2f8da8e03e34d81af7b7f510dc9eba9 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-b2f8da8e03e34d81af7b7f510dc9eba92025-08-20T03:48:18ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01760-2A fault diagnosis method for inter-turn short circuit based on magnetic field distributionBowen Wang0Lulu Wang1College of Information Science and Engineering, Northeastern UniversityCollege of Education, Linyi UniversityAbstract Inter-turn short circuit (ITSC) faults are among the most critical and frequent failures in power transformer windings. However, conducting a quantitative analysis of the winding insulation state based on MFL remains challenging. This paper proposes a magnetic-electrical spatial state model that links local fault currents to leakage magnetic field variations. A data-driven fault localization framework is developed by combining recursive feature elimination (RFE), Spearman correlation analysis, and support vector machine (SVM) classification. Experimental validation on a 3 kW dry-type transformer, enhanced with FEM-based signal augmentation, shows that the method achieves 97.4% fault localization accuracy under rated load using only 20 Hall-effect sensors. Under no-load conditions, the accuracy remains 92.3%, demonstrating robustness against weak excitation and electromagnetic noise. The optimized sensor layout in the winding gap enhances spatial sensitivity while minimizing hardware complexity. These results confirm the method’s potential for scalable, non-intrusive insulation monitoring in practical power transformers.https://doi.org/10.1038/s41598-025-01760-2Inter-turn short circuitFault localizationMagnetic flux leakageOptimal sensor placement |
| spellingShingle | Bowen Wang Lulu Wang A fault diagnosis method for inter-turn short circuit based on magnetic field distribution Scientific Reports Inter-turn short circuit Fault localization Magnetic flux leakage Optimal sensor placement |
| title | A fault diagnosis method for inter-turn short circuit based on magnetic field distribution |
| title_full | A fault diagnosis method for inter-turn short circuit based on magnetic field distribution |
| title_fullStr | A fault diagnosis method for inter-turn short circuit based on magnetic field distribution |
| title_full_unstemmed | A fault diagnosis method for inter-turn short circuit based on magnetic field distribution |
| title_short | A fault diagnosis method for inter-turn short circuit based on magnetic field distribution |
| title_sort | fault diagnosis method for inter turn short circuit based on magnetic field distribution |
| topic | Inter-turn short circuit Fault localization Magnetic flux leakage Optimal sensor placement |
| url | https://doi.org/10.1038/s41598-025-01760-2 |
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