Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems
<p>Wind energy (WE) is one of the most important technology to produce energy and an efficient source of renewable energy (RE) available in the atmospheric environment due to different air-currents spread over the stratosphere and troposphere. Wind energy conversion (WEC) system has become a f...
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| مؤلفون آخرون: | , , , |
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2022
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| _version_ | 1864513536528482304 |
|---|---|
| author | Khaled Dhibi (16891524) |
| author2 | Majdi Mansouri (16869885) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author author |
| author_facet | Khaled Dhibi (16891524) Majdi Mansouri (16869885) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Khaled Dhibi (16891524) Majdi Mansouri (16869885) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2022-07-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.renene.2022.05.082 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Reduced_neural_network_based_ensemble_approach_for_fault_detection_and_diagnosis_of_wind_energy_converter_systems/24720333 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning Neural network (NN) Fault detection and diagnosis (FDD) Ensemble learning (EL) Hierarchical K-Means (H–K-Means) Wind energy conversion (WEC) |
| dc.title.none.fl_str_mv | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Wind energy (WE) is one of the most important technology to produce energy and an efficient source of renewable energy (RE) available in the atmospheric environment due to different air-currents spread over the stratosphere and troposphere. Wind energy conversion (WEC) system has become a focal point in the research of RE in recent years. Moreover, fault detection and diagnosis (FDD) plays an important role in ensuring WEC safety. In the past decades, neural networks (NN) has provided an effective performance in fault diagnosis. On the other hand, ensemble learning (EL) techniques have gained significant attention from the scientific community. EL is a technique that creates and combines multiple machine learning models in order to produce one optimal predictive model which gives improved results. The goal of this paper is to develop and validate effective neural networks based ensemble approach. First, an ensemble classifier based on neural networks techniques and using bagging, boosting, and random subspace combination techniques is proposed. Second, an improved extension of the proposed neural networks-based ensemble technique is presented. Finally, the results obtained from the proposed neural networks-based ensemble techniques are compared with other methods to illustrate and validate the advantages of the proposed techniques.</p><h2>Other Information</h2> <p> Published in: Renewable Energy<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.renene.2022.05.082" target="_blank">https://dx.doi.org/10.1016/j.renene.2022.05.082</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a14edfaef17bf42f83f757d86e8d3fde |
| identifier_str_mv | 10.1016/j.renene.2022.05.082 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24720333 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systemsKhaled Dhibi (16891524)Majdi Mansouri (16869885)Kais Bouzrara (16869906)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringEnvironmental engineeringInformation and computing sciencesMachine learningNeural network (NN)Fault detection and diagnosis (FDD)Ensemble learning (EL)Hierarchical K-Means (H–K-Means)Wind energy conversion (WEC)<p>Wind energy (WE) is one of the most important technology to produce energy and an efficient source of renewable energy (RE) available in the atmospheric environment due to different air-currents spread over the stratosphere and troposphere. Wind energy conversion (WEC) system has become a focal point in the research of RE in recent years. Moreover, fault detection and diagnosis (FDD) plays an important role in ensuring WEC safety. In the past decades, neural networks (NN) has provided an effective performance in fault diagnosis. On the other hand, ensemble learning (EL) techniques have gained significant attention from the scientific community. EL is a technique that creates and combines multiple machine learning models in order to produce one optimal predictive model which gives improved results. The goal of this paper is to develop and validate effective neural networks based ensemble approach. First, an ensemble classifier based on neural networks techniques and using bagging, boosting, and random subspace combination techniques is proposed. Second, an improved extension of the proposed neural networks-based ensemble technique is presented. Finally, the results obtained from the proposed neural networks-based ensemble techniques are compared with other methods to illustrate and validate the advantages of the proposed techniques.</p><h2>Other Information</h2> <p> Published in: Renewable Energy<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.renene.2022.05.082" target="_blank">https://dx.doi.org/10.1016/j.renene.2022.05.082</a></p>2022-07-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.renene.2022.05.082https://figshare.com/articles/journal_contribution/Reduced_neural_network_based_ensemble_approach_for_fault_detection_and_diagnosis_of_wind_energy_converter_systems/24720333CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/247203332022-07-01T00:00:00Z |
| spellingShingle | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems Khaled Dhibi (16891524) Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning Neural network (NN) Fault detection and diagnosis (FDD) Ensemble learning (EL) Hierarchical K-Means (H–K-Means) Wind energy conversion (WEC) |
| status_str | publishedVersion |
| title | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| title_full | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| title_fullStr | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| title_full_unstemmed | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| title_short | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| title_sort | Reduced neural network based ensemble approach for fault detection and diagnosis of wind energy converter systems |
| topic | Engineering Electrical engineering Environmental engineering Information and computing sciences Machine learning Neural network (NN) Fault detection and diagnosis (FDD) Ensemble learning (EL) Hierarchical K-Means (H–K-Means) Wind energy conversion (WEC) |