Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques
To maintain the security of transformer differential protection, it is essential to restrain its response to oversetting differential current caused by the inrush current or other switching conditions. This paper presents a new proposed method to discriminate the transformer’s internal fault from th...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/3986400 |
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| author | Abedalgany Abedallah Athamneh Ali Mohammad Alqudah |
| author_facet | Abedalgany Abedallah Athamneh Ali Mohammad Alqudah |
| author_sort | Abedalgany Abedallah Athamneh |
| collection | DOAJ |
| description | To maintain the security of transformer differential protection, it is essential to restrain its response to oversetting differential current caused by the inrush current or other switching conditions. This paper presents a new proposed method to discriminate the transformer’s internal fault from the inrush current; the discrimination process is based on a convolutional neural network (CNN) with a combination of the higher order spectral estimations that perform a deep learning classification with high accuracy. This research succeeded in proposing two robust and efficient CNN models; the first one is the 1D CNN, which takes the sole signal without any transformation, while the second model is the 2D CNN, which takes the short-time Fourier transform of the signal. Both developed models are light and have the minimum number of layers that can achieve a very high performance. The performance of the proposed models is tested using a dataset prepared according to laboratory-measured transformer parameters. The results are compared with other well-known methods, and the achievement of high numerical performance evaluation validates the consistency of the proposed methods. |
| format | Article |
| id | doaj-art-09cef631bda94dd88aa1c01c056be829 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-09cef631bda94dd88aa1c01c056be8292025-02-03T00:09:51ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/3986400Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network TechniquesAbedalgany Abedallah Athamneh0Ali Mohammad Alqudah1Department of Electrical Power EngineeringDepartment of Biomedical Systems and Informatics EngineeringTo maintain the security of transformer differential protection, it is essential to restrain its response to oversetting differential current caused by the inrush current or other switching conditions. This paper presents a new proposed method to discriminate the transformer’s internal fault from the inrush current; the discrimination process is based on a convolutional neural network (CNN) with a combination of the higher order spectral estimations that perform a deep learning classification with high accuracy. This research succeeded in proposing two robust and efficient CNN models; the first one is the 1D CNN, which takes the sole signal without any transformation, while the second model is the 2D CNN, which takes the short-time Fourier transform of the signal. Both developed models are light and have the minimum number of layers that can achieve a very high performance. The performance of the proposed models is tested using a dataset prepared according to laboratory-measured transformer parameters. The results are compared with other well-known methods, and the achievement of high numerical performance evaluation validates the consistency of the proposed methods.http://dx.doi.org/10.1155/2024/3986400 |
| spellingShingle | Abedalgany Abedallah Athamneh Ali Mohammad Alqudah Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques Journal of Electrical and Computer Engineering |
| title | Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques |
| title_full | Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques |
| title_fullStr | Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques |
| title_full_unstemmed | Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques |
| title_short | Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques |
| title_sort | transformer inrush current and internal fault discrimination using multitypes of convolutional neural network techniques |
| url | http://dx.doi.org/10.1155/2024/3986400 |
| work_keys_str_mv | AT abedalganyabedallahathamneh transformerinrushcurrentandinternalfaultdiscriminationusingmultitypesofconvolutionalneuralnetworktechniques AT alimohammadalqudah transformerinrushcurrentandinternalfaultdiscriminationusingmultitypesofconvolutionalneuralnetworktechniques |