Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques
Power transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, inclu...
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2024-12-01
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| author | Sahar R. Al-Sakini Ghassan A. Bilal Ahmed T. Sadiq Wisam Abed Kattea Al-Maliki |
| author_facet | Sahar R. Al-Sakini Ghassan A. Bilal Ahmed T. Sadiq Wisam Abed Kattea Al-Maliki |
| author_sort | Sahar R. Al-Sakini |
| collection | DOAJ |
| description | Power transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, including the Duval triangle method (DTM), the Doernenburg ratio method (DRM), and the Rogers ratio method (RRM), which are suitable for on-line diagnosis of transformers. The imbalanced, insufficient, and overlapping state of gas-decomposed DGA datasets, however, remains a limitation to the deployment of a powerful and accurate diagnosis approach. This study presents a new approach for transformer fault diagnosis based on DGA, one which aims to improve the performance evaluation criteria to predict current faults and to lower the associated costs. We used six optimized machine learning methods (MLMs) for dataset transformation to organize the dataset. The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). The MLMs were implemented by using 628 dataset samples, which were obtained from laboratories, other studies, and electricity stations in Iraq. Accordingly, 502 dataset samples constituted the training set while the remaining 126 dataset samples served as the testing set. The results were examined according to six important measurements (accuracy ratio, precision, recall, specificity, F1 score, and Matthews correlation coefficient (MCC)). The best results were found for case A with RF (95.2%). In cases B and C, the best results were found for RF and DT (100% and 99.2%, respectively). With respect to the advanced machine learning method, the transformer fault diagnosis based on the MLMs was exceedingly more accurate in its predictions than the conventional and artificial intelligence-based methods. |
| format | Article |
| id | doaj-art-3c2d364dde9a482f9208c7a65db2526b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3c2d364dde9a482f9208c7a65db2526b2025-01-10T13:14:30ZengMDPI AGApplied Sciences2076-34172024-12-0115111810.3390/app15010118Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning TechniquesSahar R. Al-Sakini0Ghassan A. Bilal1Ahmed T. Sadiq2Wisam Abed Kattea Al-Maliki3Department of Electromechanical Engineering, University of Technology-Iraq, Ministry of Higher Education and Scientific Research, Baghdad 10066, IraqDepartment of Electromechanical Engineering, University of Technology-Iraq, Ministry of Higher Education and Scientific Research, Baghdad 10066, IraqComputer Sciences Department, University of Technology-Iraq, Ministry of Higher Education and Scientific Research, Baghdad 10066, IraqMechanical Engineering Department, University of Technology-Iraq, Ministry of Higher Education and Scientific Research, Baghdad 10066, IraqPower transformers are one of the most important elements of electrical power systems. The fast diagnosis of transformer faults improves the efficiency of power systems. One of the most favored methodologies for transformer fault diagnostics is based on dissolved gas analysis (DGA) techniques, including the Duval triangle method (DTM), the Doernenburg ratio method (DRM), and the Rogers ratio method (RRM), which are suitable for on-line diagnosis of transformers. The imbalanced, insufficient, and overlapping state of gas-decomposed DGA datasets, however, remains a limitation to the deployment of a powerful and accurate diagnosis approach. This study presents a new approach for transformer fault diagnosis based on DGA, one which aims to improve the performance evaluation criteria to predict current faults and to lower the associated costs. We used six optimized machine learning methods (MLMs) for dataset transformation to organize the dataset. The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). The MLMs were implemented by using 628 dataset samples, which were obtained from laboratories, other studies, and electricity stations in Iraq. Accordingly, 502 dataset samples constituted the training set while the remaining 126 dataset samples served as the testing set. The results were examined according to six important measurements (accuracy ratio, precision, recall, specificity, F1 score, and Matthews correlation coefficient (MCC)). The best results were found for case A with RF (95.2%). In cases B and C, the best results were found for RF and DT (100% and 99.2%, respectively). With respect to the advanced machine learning method, the transformer fault diagnosis based on the MLMs was exceedingly more accurate in its predictions than the conventional and artificial intelligence-based methods.https://www.mdpi.com/2076-3417/15/1/118fault predictiontransformer fault diagnosticDGAmachine learningconventional methods |
| spellingShingle | Sahar R. Al-Sakini Ghassan A. Bilal Ahmed T. Sadiq Wisam Abed Kattea Al-Maliki Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques Applied Sciences fault prediction transformer fault diagnostic DGA machine learning conventional methods |
| title | Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques |
| title_full | Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques |
| title_fullStr | Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques |
| title_full_unstemmed | Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques |
| title_short | Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques |
| title_sort | dissolved gas analysis for fault prediction in power transformers using machine learning techniques |
| topic | fault prediction transformer fault diagnostic DGA machine learning conventional methods |
| url | https://www.mdpi.com/2076-3417/15/1/118 |
| work_keys_str_mv | AT saharralsakini dissolvedgasanalysisforfaultpredictioninpowertransformersusingmachinelearningtechniques AT ghassanabilal dissolvedgasanalysisforfaultpredictioninpowertransformersusingmachinelearningtechniques AT ahmedtsadiq dissolvedgasanalysisforfaultpredictioninpowertransformersusingmachinelearningtechniques AT wisamabedkatteaalmaliki dissolvedgasanalysisforfaultpredictioninpowertransformersusingmachinelearningtechniques |