Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks
<p dir="ltr">Microgrid control and operation depend on fault detection and classification because it allows quick fault separation and recovery. Due to their reliance on sizable fault currents, classic fault detection techniques are no longer suitable for microgrids that employ inver...
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2024
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| author | S. N. V. Bramareswara Rao (21768302) |
| author2 | Y. V. Pavan Kumar (17984125) Mohammad Amir (12418899) S. M. Muyeen (14778337) |
| author2_role | author author author |
| author_facet | S. N. V. Bramareswara Rao (21768302) Y. V. Pavan Kumar (17984125) Mohammad Amir (12418899) S. M. Muyeen (14778337) |
| author_role | author |
| dc.creator.none.fl_str_mv | S. N. V. Bramareswara Rao (21768302) Y. V. Pavan Kumar (17984125) Mohammad Amir (12418899) S. M. Muyeen (14778337) |
| dc.date.none.fl_str_mv | 2024-03-28T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s00202-024-02329-4 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Fault_detection_and_classification_in_hybrid_energy-based_multi-area_grid-connected_microgrid_clusters_using_discrete_wavelet_transform_with_deep_neural_networks/29625272 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Deep learning algorithm Deep neural networks Discrete wavelet transform Hybrid energy sources Microgrids Wavelet transform |
| dc.title.none.fl_str_mv | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Microgrid control and operation depend on fault detection and classification because it allows quick fault separation and recovery. Due to their reliance on sizable fault currents, classic fault detection techniques are no longer suitable for microgrids that employ inverter-interfaced distributed generation. Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. They enable quick responses to faults, reduce downtime, enhance energy efficiency, and contribute to the overall sustainability and resilience of microgrids. With this intent, this work proposes a “Discrete Wavelet Transform with Deep Neural Network (DWT-DNN)” for detecting and classifying the various faults that occurred in hybrid energy-based multi-area grid-connected microgrid clusters. The proposed DWT-DNN first extracts the input features from the point of common coupling of the cluster system using DWT, and then, these decomposed features are applied as input variables to train the DNN for the detection and classification of various faults. All the investigations are performed in the “MATLAB/Simulink 2022a” environment. To validate the effectiveness of the proposed DWT-DNN, the results are compared with wavelet packet transforms (WPT) in terms of accuracy in detecting and classifying the faults. From the simulation findings and observations, it is evident that the proposed DNN produced fruitful results.</p><h2>Other Information</h2><p dir="ltr">Published in: Electrical Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00202-024-02329-4" target="_blank">https://dx.doi.org/10.1007/s00202-024-02329-4</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_8169fe3fe51b55554ef6a28d56aaec2b |
| identifier_str_mv | 10.1007/s00202-024-02329-4 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29625272 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networksS. N. V. Bramareswara Rao (21768302)Y. V. Pavan Kumar (17984125)Mohammad Amir (12418899)S. M. Muyeen (14778337)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningDeep learning algorithmDeep neural networksDiscrete wavelet transformHybrid energy sourcesMicrogridsWavelet transform<p dir="ltr">Microgrid control and operation depend on fault detection and classification because it allows quick fault separation and recovery. Due to their reliance on sizable fault currents, classic fault detection techniques are no longer suitable for microgrids that employ inverter-interfaced distributed generation. Nowadays, deep learning algorithms are essential for ensuring the reliable, safe, and efficient operation of these complex energy systems. They enable quick responses to faults, reduce downtime, enhance energy efficiency, and contribute to the overall sustainability and resilience of microgrids. With this intent, this work proposes a “Discrete Wavelet Transform with Deep Neural Network (DWT-DNN)” for detecting and classifying the various faults that occurred in hybrid energy-based multi-area grid-connected microgrid clusters. The proposed DWT-DNN first extracts the input features from the point of common coupling of the cluster system using DWT, and then, these decomposed features are applied as input variables to train the DNN for the detection and classification of various faults. All the investigations are performed in the “MATLAB/Simulink 2022a” environment. To validate the effectiveness of the proposed DWT-DNN, the results are compared with wavelet packet transforms (WPT) in terms of accuracy in detecting and classifying the faults. From the simulation findings and observations, it is evident that the proposed DNN produced fruitful results.</p><h2>Other Information</h2><p dir="ltr">Published in: Electrical Engineering<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00202-024-02329-4" target="_blank">https://dx.doi.org/10.1007/s00202-024-02329-4</a></p>2024-03-28T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s00202-024-02329-4https://figshare.com/articles/journal_contribution/Fault_detection_and_classification_in_hybrid_energy-based_multi-area_grid-connected_microgrid_clusters_using_discrete_wavelet_transform_with_deep_neural_networks/29625272CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296252722024-03-28T09:00:00Z |
| spellingShingle | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks S. N. V. Bramareswara Rao (21768302) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Deep learning algorithm Deep neural networks Discrete wavelet transform Hybrid energy sources Microgrids Wavelet transform |
| status_str | publishedVersion |
| title | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| title_full | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| title_fullStr | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| title_full_unstemmed | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| title_short | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| title_sort | Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Deep learning algorithm Deep neural networks Discrete wavelet transform Hybrid energy sources Microgrids Wavelet transform |