A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence
Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime. Existing maintenance strategies often fail to accurately predict such failures, highlighting the need for no...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11053795/ |
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| author | Emrah Aslan Yildirim Ozupak Feyyaz Alpsalaz Zakaria M. S. Elbarbary |
| author_facet | Emrah Aslan Yildirim Ozupak Feyyaz Alpsalaz Zakaria M. S. Elbarbary |
| author_sort | Emrah Aslan |
| collection | DOAJ |
| description | Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime. Existing maintenance strategies often fail to accurately predict such failures, highlighting the need for novel predictive approaches. This study aims to improve the reliability of power systems by predicting transformer failures through the integration of IoT technologies and advanced machine learning techniques. The proposed hybrid model combines the LightGBM algorithm with GridSearch optimization to achieve both high predictive accuracy and computational efficiency. In addition, the model enhances interpretability by incorporating SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for transparent decision making. The study presents a detailed comparison of different classification algorithms and evaluates their performance using metrics such as accuracy, recall, and F1 score. The results show that the hybrid model outperforms other methods, achieving an accuracy of 99.91%. The SHAP and LIME analyses provide engineers and researchers with valuable insights by highlighting the most influential features in failure prediction. In addition, the model’s ability to efficiently handle large data sets enhances its practicality in real-world power systems. By proposing an innovative approach to failure prediction, this research contributes to both the theoretical foundation and practical advancement of sustainable and reliable energy infrastructures. |
| format | Article |
| id | doaj-art-d4281d4bc81e46d28435c9a48bbe6d3f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d4281d4bc81e46d28435c9a48bbe6d3f2025-08-20T03:15:35ZengIEEEIEEE Access2169-35362025-01-011311361811363310.1109/ACCESS.2025.358377311053795A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial IntelligenceEmrah Aslan0https://orcid.org/0000-0002-0181-3658Yildirim Ozupak1https://orcid.org/0000-0001-8461-8702Feyyaz Alpsalaz2https://orcid.org/0000-0002-7695-6426Zakaria M. S. Elbarbary3https://orcid.org/0000-0003-1750-9244Department of Computer Engineering, Faculty of Engineering and Architecture, Mardin Artuklu University, Mardin, TürkiyeDepartment of Electricity and Energy, Silvan Vocational School, Dicle University, Diyarbakır, TürkiyeDepartment of Electricity and Energy, Akdagmadeni Vocational School, Yozgat Bozok University, Yozgat, TürkiyeElectrical Engineering Department, Faculty of Engineering, King Khalid University, Abha, Saudi ArabiaPower transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime. Existing maintenance strategies often fail to accurately predict such failures, highlighting the need for novel predictive approaches. This study aims to improve the reliability of power systems by predicting transformer failures through the integration of IoT technologies and advanced machine learning techniques. The proposed hybrid model combines the LightGBM algorithm with GridSearch optimization to achieve both high predictive accuracy and computational efficiency. In addition, the model enhances interpretability by incorporating SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) for transparent decision making. The study presents a detailed comparison of different classification algorithms and evaluates their performance using metrics such as accuracy, recall, and F1 score. The results show that the hybrid model outperforms other methods, achieving an accuracy of 99.91%. The SHAP and LIME analyses provide engineers and researchers with valuable insights by highlighting the most influential features in failure prediction. In addition, the model’s ability to efficiently handle large data sets enhances its practicality in real-world power systems. By proposing an innovative approach to failure prediction, this research contributes to both the theoretical foundation and practical advancement of sustainable and reliable energy infrastructures.https://ieeexplore.ieee.org/document/11053795/Fault detectionpower transformersmachine learningSHAPLIME |
| spellingShingle | Emrah Aslan Yildirim Ozupak Feyyaz Alpsalaz Zakaria M. S. Elbarbary A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence IEEE Access Fault detection power transformers machine learning SHAP LIME |
| title | A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence |
| title_full | A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence |
| title_fullStr | A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence |
| title_full_unstemmed | A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence |
| title_short | A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence |
| title_sort | hybrid machine learning approach for predicting power transformer failures using internet of things based monitoring and explainable artificial intelligence |
| topic | Fault detection power transformers machine learning SHAP LIME |
| url | https://ieeexplore.ieee.org/document/11053795/ |
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