Robust fault detection and classification in power transmission lines via ensemble machine learning models
Abstract Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and curr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-86554-2 |
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| author | Tahir Anwar Chaoxu Mu Muhammad Zain Yousaf Wajid Khan Saqib Khalid Ahmad O. Hourani Ievgen Zaitsev |
| author_facet | Tahir Anwar Chaoxu Mu Muhammad Zain Yousaf Wajid Khan Saqib Khalid Ahmad O. Hourani Ievgen Zaitsev |
| author_sort | Tahir Anwar |
| collection | DOAJ |
| description | Abstract Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. An ensemble methodology, RF-LSTM Tuned KNN, is proposed to enhance detection accuracy and robustness. Results indicate that RF-LSTM Tuned KNN achieves a remarkable accuracy of 99.96% on a multi-label dataset, outperforming RF (97.50%) and KNN (96.55%). In binary classification, KNN attains the highest accuracy of 99.85%, closely followed by RF at 99.72%. This methodology provides significant advancements in fault detection capabilities, offering valuable insights for improving grid reliability and stability, and ensuring a more resilient power supply. |
| format | Article |
| id | doaj-art-ed08382622074769b038044fe0161ccc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-ed08382622074769b038044fe0161ccc2025-01-26T12:24:13ZengNature PortfolioScientific Reports2045-23222025-01-0115111710.1038/s41598-025-86554-2Robust fault detection and classification in power transmission lines via ensemble machine learning modelsTahir Anwar0Chaoxu Mu1Muhammad Zain Yousaf2Wajid Khan3Saqib Khalid4Ahmad O. Hourani5Ievgen Zaitsev6School of Electrical and Information Engineering, Tianjin UniversitySchool of Electrical and Information Engineering, Tianjin UniversityCenter for Renewable Energy and Microgrids, Huanjiang Laboratory, Zhejiang UniversitySchool of Electrical and Information Engineering, Tianjin UniversitySchool of Electrical Engineering, The University of LahoreHourani Center for Applied Scientific Research, Al-Ahliyya Amman UniversityDepartment of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of UkraineAbstract Transmission lines are vital for delivering electricity over long distances, yet they face reliability challenges due to faults that can disrupt power supply and pose safety risks. This research introduces a novel approach for fault detection and classification by analyzing voltage and current patterns across transmission line phases. Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. An ensemble methodology, RF-LSTM Tuned KNN, is proposed to enhance detection accuracy and robustness. Results indicate that RF-LSTM Tuned KNN achieves a remarkable accuracy of 99.96% on a multi-label dataset, outperforming RF (97.50%) and KNN (96.55%). In binary classification, KNN attains the highest accuracy of 99.85%, closely followed by RF at 99.72%. This methodology provides significant advancements in fault detection capabilities, offering valuable insights for improving grid reliability and stability, and ensuring a more resilient power supply.https://doi.org/10.1038/s41598-025-86554-2Transmission linesFault detectionMachine learningEnsemble learningPower stability |
| spellingShingle | Tahir Anwar Chaoxu Mu Muhammad Zain Yousaf Wajid Khan Saqib Khalid Ahmad O. Hourani Ievgen Zaitsev Robust fault detection and classification in power transmission lines via ensemble machine learning models Scientific Reports Transmission lines Fault detection Machine learning Ensemble learning Power stability |
| title | Robust fault detection and classification in power transmission lines via ensemble machine learning models |
| title_full | Robust fault detection and classification in power transmission lines via ensemble machine learning models |
| title_fullStr | Robust fault detection and classification in power transmission lines via ensemble machine learning models |
| title_full_unstemmed | Robust fault detection and classification in power transmission lines via ensemble machine learning models |
| title_short | Robust fault detection and classification in power transmission lines via ensemble machine learning models |
| title_sort | robust fault detection and classification in power transmission lines via ensemble machine learning models |
| topic | Transmission lines Fault detection Machine learning Ensemble learning Power stability |
| url | https://doi.org/10.1038/s41598-025-86554-2 |
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