On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach
<p>Transmission lines (TLs) of power networks are often encountered with a number of faults. To continue normal operation and reduce the damage due to the TL faults, it is a must to identify and classify faults as early as possible. In this paper, the design and development of an intelligent m...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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إضافة وسم
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| _version_ | 1864513537496317952 |
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| author | Md. Sihab Uddin (17542488) |
| author2 | Md. Zahid Hossain (17480529) Shahriar Rahman Fahim (17542491) Subrata K. Sarker (16904556) Erphan Ahmmad Bhuiyan (17542494) S.M. Muyeen (15746160) Sajal K. Das (7185962) |
| author2_role | author author author author author author |
| author_facet | Md. Sihab Uddin (17542488) Md. Zahid Hossain (17480529) Shahriar Rahman Fahim (17542491) Subrata K. Sarker (16904556) Erphan Ahmmad Bhuiyan (17542494) S.M. Muyeen (15746160) Sajal K. Das (7185962) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Sihab Uddin (17542488) Md. Zahid Hossain (17480529) Shahriar Rahman Fahim (17542491) Subrata K. Sarker (16904556) Erphan Ahmmad Bhuiyan (17542494) S.M. Muyeen (15746160) Sajal K. Das (7185962) |
| dc.date.none.fl_str_mv | 2022-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.egyr.2022.07.163 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/On_the_protection_of_power_system_Transmission_line_fault_analysis_based_on_an_optimal_machine_learning_approach/24717753 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Information and computing sciences Machine learning Transmission line Supervised learning Fault diagnosis Wavelet transform Optimized model Noise immunity |
| dc.title.none.fl_str_mv | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Transmission lines (TLs) of power networks are often encountered with a number of faults. To continue normal operation and reduce the damage due to the TL faults, it is a must to identify and classify faults as early as possible. In this paper, the design and development of an intelligent machine learning framework is presented to identify and classify faults in a power TL. The design of the proposed framework is done with the goal of reducing computational load and ensuring resilience against source noise, source impedance, fault strength, and sampling frequency variation. The design is carried out based on the selection of the optimal model parameters using a search optimization algorithm called GridSearchCV. The effectiveness of the proposed model is verified by testing the model on the IEC standard microgrid model in a MATLAB environment. The results show that the proposed model has more than ninety-nine per cent overall accuracy in the identification and classification of the TL faults. The results are also compared with some state-of-the-art approaches such as LSTM, RNN, DBN, DRL, and CNF to further examine the performance of the proposed framework. The comparison demonstrates that the proposed model outperforms other existing techniques in terms of accuracy, computational cost, and response speed.</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2022.07.163" target="_blank">https://dx.doi.org/10.1016/j.egyr.2022.07.163</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3b1f54306fa59a12b9e0d026a6de0e96 |
| identifier_str_mv | 10.1016/j.egyr.2022.07.163 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24717753 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approachMd. Sihab Uddin (17542488)Md. Zahid Hossain (17480529)Shahriar Rahman Fahim (17542491)Subrata K. Sarker (16904556)Erphan Ahmmad Bhuiyan (17542494)S.M. Muyeen (15746160)Sajal K. Das (7185962)EngineeringElectrical engineeringInformation and computing sciencesMachine learningTransmission lineSupervised learningFault diagnosisWavelet transformOptimized modelNoise immunity<p>Transmission lines (TLs) of power networks are often encountered with a number of faults. To continue normal operation and reduce the damage due to the TL faults, it is a must to identify and classify faults as early as possible. In this paper, the design and development of an intelligent machine learning framework is presented to identify and classify faults in a power TL. The design of the proposed framework is done with the goal of reducing computational load and ensuring resilience against source noise, source impedance, fault strength, and sampling frequency variation. The design is carried out based on the selection of the optimal model parameters using a search optimization algorithm called GridSearchCV. The effectiveness of the proposed model is verified by testing the model on the IEC standard microgrid model in a MATLAB environment. The results show that the proposed model has more than ninety-nine per cent overall accuracy in the identification and classification of the TL faults. The results are also compared with some state-of-the-art approaches such as LSTM, RNN, DBN, DRL, and CNF to further examine the performance of the proposed framework. The comparison demonstrates that the proposed model outperforms other existing techniques in terms of accuracy, computational cost, and response speed.</p><h2>Other Information</h2> <p> Published in: Energy Reports<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.egyr.2022.07.163" target="_blank">https://dx.doi.org/10.1016/j.egyr.2022.07.163</a></p>2022-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.egyr.2022.07.163https://figshare.com/articles/journal_contribution/On_the_protection_of_power_system_Transmission_line_fault_analysis_based_on_an_optimal_machine_learning_approach/24717753CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247177532022-11-01T00:00:00Z |
| spellingShingle | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach Md. Sihab Uddin (17542488) Engineering Electrical engineering Information and computing sciences Machine learning Transmission line Supervised learning Fault diagnosis Wavelet transform Optimized model Noise immunity |
| status_str | publishedVersion |
| title | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| title_full | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| title_fullStr | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| title_full_unstemmed | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| title_short | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| title_sort | On the protection of power system: Transmission line fault analysis based on an optimal machine learning approach |
| topic | Engineering Electrical engineering Information and computing sciences Machine learning Transmission line Supervised learning Fault diagnosis Wavelet transform Optimized model Noise immunity |