Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation
Classification is one of the most common methods of supervised learning, which is divided into a process of data acquisition, data mining, feature analysis, machine learning algorithm selection, model learning and validation, as well as prediction of the result, which was done in the current work. T...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/1/201 |
| _version_ | 1797625870439940096 |
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| author | Aleksandra Płużek Łukasz Nagi |
| author_facet | Aleksandra Płużek Łukasz Nagi |
| author_sort | Aleksandra Płużek |
| collection | DOAJ |
| description | Classification is one of the most common methods of supervised learning, which is divided into a process of data acquisition, data mining, feature analysis, machine learning algorithm selection, model learning and validation, as well as prediction of the result, which was done in the current work. The data that were analyzed concerned ionizing radiation signals generated by partial discharges, recorded by a method using the phenomenon of scintillation. It was decided to check if the data could be classified and if it was possible to determine the defect of an electrical power device. It was possible to find out which classifier (algorithm) worked best for the task, and that the data obtained can be classified, as well as that it is possible to determine the defect. In addition, it was possible to check what effect changing the default values of the classifier’s parameters has on the effectiveness of classification. |
| first_indexed | 2024-03-11T10:02:35Z |
| format | Article |
| id | doaj.art-0e1c31c402a74a70b4819057190493fb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T10:02:35Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-0e1c31c402a74a70b4819057190493fb2023-11-16T15:15:57ZengMDPI AGEnergies1996-10732022-12-0116120110.3390/en16010201Classification of Partial Discharges Recorded by the Method Using the Phenomenon of ScintillationAleksandra Płużek0Łukasz Nagi1Department of Electrical Power and Renewable Energy, Faculty of Electrical Engineering Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, PolandDepartment of Electrical Power and Renewable Energy, Faculty of Electrical Engineering Automatic Control and Informatics, Opole University of Technology, 45-758 Opole, PolandClassification is one of the most common methods of supervised learning, which is divided into a process of data acquisition, data mining, feature analysis, machine learning algorithm selection, model learning and validation, as well as prediction of the result, which was done in the current work. The data that were analyzed concerned ionizing radiation signals generated by partial discharges, recorded by a method using the phenomenon of scintillation. It was decided to check if the data could be classified and if it was possible to determine the defect of an electrical power device. It was possible to find out which classifier (algorithm) worked best for the task, and that the data obtained can be classified, as well as that it is possible to determine the defect. In addition, it was possible to check what effect changing the default values of the classifier’s parameters has on the effectiveness of classification.https://www.mdpi.com/1996-1073/16/1/201classificationmachine learningpartial dischargesscintillation |
| spellingShingle | Aleksandra Płużek Łukasz Nagi Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation Energies classification machine learning partial discharges scintillation |
| title | Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation |
| title_full | Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation |
| title_fullStr | Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation |
| title_full_unstemmed | Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation |
| title_short | Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation |
| title_sort | classification of partial discharges recorded by the method using the phenomenon of scintillation |
| topic | classification machine learning partial discharges scintillation |
| url | https://www.mdpi.com/1996-1073/16/1/201 |
| work_keys_str_mv | AT aleksandrapłuzek classificationofpartialdischargesrecordedbythemethodusingthephenomenonofscintillation AT łukasznagi classificationofpartialdischargesrecordedbythemethodusingthephenomenonofscintillation |