Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models
Abstract Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage...
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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/s41598-025-03033-4 |
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| author | Sherif S. M. Ghoneim Mohammed Baz Ali Alzaed Yohannes Tesfaye Zewdie |
| author_facet | Sherif S. M. Ghoneim Mohammed Baz Ali Alzaed Yohannes Tesfaye Zewdie |
| author_sort | Sherif S. M. Ghoneim |
| collection | DOAJ |
| description | Abstract Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955. |
| format | Article |
| id | doaj-art-96e333b8bc614ef7b3b128ab089bdf5f |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-96e333b8bc614ef7b3b128ab089bdf5f2025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-03033-4Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM modelsSherif S. M. Ghoneim0Mohammed Baz1Ali Alzaed2Yohannes Tesfaye Zewdie3Department of Electrical Engineering, College of Engineering, Taif UniversityDepartment of Computer Engineering, College of Computers and Information Technology, Taif UniversityArchitectural Engineering Program - Civil Engineering Department, Faculty of Engineering, Taif UniversityDepartment of Electrical and Computer Engineering, Faculty of Technology, Debre Markos UniversityAbstract Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955.https://doi.org/10.1038/s41598-025-03033-4Power transformerInsulating paperInsulating oilDissolved gas analysisFurnic compoundsXGBoost/LightGBM |
| spellingShingle | Sherif S. M. Ghoneim Mohammed Baz Ali Alzaed Yohannes Tesfaye Zewdie Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models Scientific Reports Power transformer Insulating paper Insulating oil Dissolved gas analysis Furnic compounds XGBoost/LightGBM |
| title | Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models |
| title_full | Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models |
| title_fullStr | Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models |
| title_full_unstemmed | Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models |
| title_short | Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models |
| title_sort | predicting the insulating paper state of the power transformer based on xgboost lightgbm models |
| topic | Power transformer Insulating paper Insulating oil Dissolved gas analysis Furnic compounds XGBoost/LightGBM |
| url | https://doi.org/10.1038/s41598-025-03033-4 |
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