Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data
Fault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge o...
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MDPI AG
2025-01-01
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| Series: | Eng |
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| Online Access: | https://www.mdpi.com/2673-4117/6/1/4 |
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| author | Abdallah El Ghaly |
| author_facet | Abdallah El Ghaly |
| author_sort | Abdallah El Ghaly |
| collection | DOAJ |
| description | Fault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge of effective fault classification, particularly when dealing with smaller, more practical datasets. Initially, the study examined the performance of conventional machine learning algorithms on a comprehensive dataset of 7681 samples, demonstrating high accuracy owing to the inherent symmetry of sinusoidal voltage and current signals. However, the true efficacy of these algorithms was evaluated by minimizing the dataset to 231 training samples, with the remainder being used for testing. A novel Multi-Target Ensemble Classifier was developed to improve classification accuracy. The proposed algorithm achieved an impressive overall accuracy of 0.829165, outperforming traditional methods, including the K-Nearest Neighbors Classifier, support vector classification, random forest classifier, decision tree classifier, AdaBoost classifier, gradient boosting classifier, and Gaussian NB. This research highlights the importance of efficient fault classification techniques in power systems and proposes a superior solution in the form of a multitarget ensemble classifier. |
| format | Article |
| id | doaj-art-831b0da5903f4f5ea22d5e799693efe1 |
| institution | Kabale University |
| issn | 2673-4117 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Eng |
| spelling | doaj-art-831b0da5903f4f5ea22d5e799693efe12025-01-24T13:31:32ZengMDPI AGEng2673-41172025-01-0161410.3390/eng6010004Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited DataAbdallah El Ghaly0ECE Department, Faculty of Engineering, Beirut Arab University, Beirut 11-5020, LebanonFault detection and classification in transmission lines are critical for maintaining the reliability and stability of electrical power systems. Quick and accurate fault detection allows for timely intervention, minimizing equipment damage, and reducing downtime. This study addresses the challenge of effective fault classification, particularly when dealing with smaller, more practical datasets. Initially, the study examined the performance of conventional machine learning algorithms on a comprehensive dataset of 7681 samples, demonstrating high accuracy owing to the inherent symmetry of sinusoidal voltage and current signals. However, the true efficacy of these algorithms was evaluated by minimizing the dataset to 231 training samples, with the remainder being used for testing. A novel Multi-Target Ensemble Classifier was developed to improve classification accuracy. The proposed algorithm achieved an impressive overall accuracy of 0.829165, outperforming traditional methods, including the K-Nearest Neighbors Classifier, support vector classification, random forest classifier, decision tree classifier, AdaBoost classifier, gradient boosting classifier, and Gaussian NB. This research highlights the importance of efficient fault classification techniques in power systems and proposes a superior solution in the form of a multitarget ensemble classifier.https://www.mdpi.com/2673-4117/6/1/4machine learningtransmission linesfault classificationelectrical power systems |
| spellingShingle | Abdallah El Ghaly Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data Eng machine learning transmission lines fault classification electrical power systems |
| title | Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data |
| title_full | Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data |
| title_fullStr | Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data |
| title_full_unstemmed | Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data |
| title_short | Hybrid ML Algorithm for Fault Classification in Transmission Lines Using Multi-Target Ensemble Classifier with Limited Data |
| title_sort | hybrid ml algorithm for fault classification in transmission lines using multi target ensemble classifier with limited data |
| topic | machine learning transmission lines fault classification electrical power systems |
| url | https://www.mdpi.com/2673-4117/6/1/4 |
| work_keys_str_mv | AT abdallahelghaly hybridmlalgorithmforfaultclassificationintransmissionlinesusingmultitargetensembleclassifierwithlimiteddata |