Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier
<p dir="ltr">This article presents a wavelet analysis-singular value decomposition (WA-SVD) based method for precise fault localization in recent power distribution networks using k-NN Classifier. The WA-SVD leverages the slime mould algorithm (SMA) and graph theory (GT) in enhancing...
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2025
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| _version_ | 1864513533101735936 |
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| author | Abhishek Raj (7245425) |
| author2 | Chandra Sekhar Mishra (22597922) S Ramana Kumar Joga (22597925) Mario Elzein (22173232) Asit Mohanty (22597928) Sneha Lika (22597931) Mohamed Metwally Mahmoud (15213516) Ahmed Mostafa Ewais (22597934) |
| author2_role | author author author author author author author |
| author_facet | Abhishek Raj (7245425) Chandra Sekhar Mishra (22597922) S Ramana Kumar Joga (22597925) Mario Elzein (22173232) Asit Mohanty (22597928) Sneha Lika (22597931) Mohamed Metwally Mahmoud (15213516) Ahmed Mostafa Ewais (22597934) |
| author_role | author |
| dc.creator.none.fl_str_mv | Abhishek Raj (7245425) Chandra Sekhar Mishra (22597922) S Ramana Kumar Joga (22597925) Mario Elzein (22173232) Asit Mohanty (22597928) Sneha Lika (22597931) Mohamed Metwally Mahmoud (15213516) Ahmed Mostafa Ewais (22597934) |
| dc.date.none.fl_str_mv | 2025-01-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.31763/ijrcs.v5i1.1543 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Wavelet_Analysis-_Singular_Value_Decomposition_Based_Method_for_Precise_Fault_Localization_in_Power_Distribution_Networks_Using_k-NN_Classifier/30588770 |
| dc.rights.none.fl_str_mv | CC BY-SA 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Fault Detection Fault Location Power Quality Monitoring Wavelet Transform k-NN |
| dc.title.none.fl_str_mv | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">This article presents a wavelet analysis-singular value decomposition (WA-SVD) based method for precise fault localization in recent power distribution networks using k-NN Classifier. The WA-SVD leverages the slime mould algorithm (SMA) and graph theory (GT) in enhancing the overall accuracy of fault localization. To validate the proposed methodology, extensive tests are conducted on various benchmark systems, including the IEEE 33-bus radial distribution system, the IEEE 33-bus meshed loop unbalanced distribution system, the IEEE 33-bus system with integrated renewable energy sources, and the IEEE 13-bus feeder test system. The results demonstrate a high fault classification accuracy of 99.08%, with an average localization error of just 1.2% of the total line length. The k-NN classifier exhibited a precision of 98.2% and a recall of 99.2%, underscoring the reliability and sensitivity of the proposed method. Additionally, the computational efficiency of the algorithm is evidenced by an average processing time of 0.0764 seconds per fault event, making it well-suited for real-time applications.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Robotics and Control Systems<br>License: <a href="https://creativecommons.org/licenses/by-sa/4.0" target="_blank">https://creativecommons.org/licenses/by-sa/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.31763/ijrcs.v5i1.1543" target="_blank">https://dx.doi.org/10.31763/ijrcs.v5i1.1543</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1e7dbe97dcbc15d1cc34feec21af0674 |
| identifier_str_mv | 10.31763/ijrcs.v5i1.1543 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30588770 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-SA 4.0 |
| spelling | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN ClassifierAbhishek Raj (7245425)Chandra Sekhar Mishra (22597922)S Ramana Kumar Joga (22597925)Mario Elzein (22173232)Asit Mohanty (22597928)Sneha Lika (22597931)Mohamed Metwally Mahmoud (15213516)Ahmed Mostafa Ewais (22597934)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learningFault DetectionFault LocationPower Quality MonitoringWavelet Transformk-NN<p dir="ltr">This article presents a wavelet analysis-singular value decomposition (WA-SVD) based method for precise fault localization in recent power distribution networks using k-NN Classifier. The WA-SVD leverages the slime mould algorithm (SMA) and graph theory (GT) in enhancing the overall accuracy of fault localization. To validate the proposed methodology, extensive tests are conducted on various benchmark systems, including the IEEE 33-bus radial distribution system, the IEEE 33-bus meshed loop unbalanced distribution system, the IEEE 33-bus system with integrated renewable energy sources, and the IEEE 13-bus feeder test system. The results demonstrate a high fault classification accuracy of 99.08%, with an average localization error of just 1.2% of the total line length. The k-NN classifier exhibited a precision of 98.2% and a recall of 99.2%, underscoring the reliability and sensitivity of the proposed method. Additionally, the computational efficiency of the algorithm is evidenced by an average processing time of 0.0764 seconds per fault event, making it well-suited for real-time applications.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Robotics and Control Systems<br>License: <a href="https://creativecommons.org/licenses/by-sa/4.0" target="_blank">https://creativecommons.org/licenses/by-sa/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.31763/ijrcs.v5i1.1543" target="_blank">https://dx.doi.org/10.31763/ijrcs.v5i1.1543</a></p>2025-01-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.31763/ijrcs.v5i1.1543https://figshare.com/articles/journal_contribution/Wavelet_Analysis-_Singular_Value_Decomposition_Based_Method_for_Precise_Fault_Localization_in_Power_Distribution_Networks_Using_k-NN_Classifier/30588770CC BY-SA 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/305887702025-01-01T00:00:00Z |
| spellingShingle | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier Abhishek Raj (7245425) Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Fault Detection Fault Location Power Quality Monitoring Wavelet Transform k-NN |
| status_str | publishedVersion |
| title | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| title_full | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| title_fullStr | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| title_full_unstemmed | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| title_short | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| title_sort | Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Fault Detection Fault Location Power Quality Monitoring Wavelet Transform k-NN |