An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization
<p dir="ltr">The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is pr...
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| مؤلفون آخرون: | , , |
| منشور في: |
2022
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| _version_ | 1864513547715739648 |
|---|---|
| author | Majdi Mansouri (16869885) |
| author2 | Khaled Dhibi (16891524) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author |
| author_facet | Majdi Mansouri (16869885) Khaled Dhibi (16891524) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Majdi Mansouri (16869885) Khaled Dhibi (16891524) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2022-09-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/su141811195 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_Effective_Fault_Diagnosis_Technique_for_Wind_Energy_Conversion_Systems_Based_on_an_Improved_Particle_Swarm_Optimization/29116943 |
| 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 Artificial intelligence Machine learning wind energy conversion (WEC) fault diagnosis particle swarm optimization (PSO) neural network (NN) euclidean distance (ED) |
| dc.title.none.fl_str_mv | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is proposed. The main idea behind the use of the PSO algorithm is to remove irrelevant features and extract only the most significant ones from raw data in order to improve the classification task using a neural networks classifier. Then, to overcome the problem of premature convergence and local sub-optimal areas when using the classical PSO optimization algorithm, an improved extension of the PSO algorithm is proposed. The basic idea behind this proposal is to use the Euclidean distance as a dissimilarity metric between observations in which a single observation is kept in case of redundancies. In addition, the proposed reduced PSO-NN (RPSO-NN) technique not only enhances the results in terms of accuracy but also provides a significant reduction in computation time and storage cost by reducing the size of the training dataset and removing irrelevant and redundant samples. The experimental results showed the robustness and high performance of the proposed diagnosis paradigms.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<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/10.3390/su141811195" target="_blank">https://dx.doi.org/10.3390/su141811195</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_b8d1a47f1999766cf732915cf8487383 |
| identifier_str_mv | 10.3390/su141811195 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29116943 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm OptimizationMajdi Mansouri (16869885)Khaled Dhibi (16891524)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceMachine learningwind energy conversion (WEC)fault diagnosisparticle swarm optimization (PSO)neural network (NN)euclidean distance (ED)<p dir="ltr">The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is proposed. The main idea behind the use of the PSO algorithm is to remove irrelevant features and extract only the most significant ones from raw data in order to improve the classification task using a neural networks classifier. Then, to overcome the problem of premature convergence and local sub-optimal areas when using the classical PSO optimization algorithm, an improved extension of the PSO algorithm is proposed. The basic idea behind this proposal is to use the Euclidean distance as a dissimilarity metric between observations in which a single observation is kept in case of redundancies. In addition, the proposed reduced PSO-NN (RPSO-NN) technique not only enhances the results in terms of accuracy but also provides a significant reduction in computation time and storage cost by reducing the size of the training dataset and removing irrelevant and redundant samples. The experimental results showed the robustness and high performance of the proposed diagnosis paradigms.</p><h2>Other Information</h2><p dir="ltr">Published in: Sustainability<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/10.3390/su141811195" target="_blank">https://dx.doi.org/10.3390/su141811195</a></p>2022-09-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/su141811195https://figshare.com/articles/journal_contribution/An_Effective_Fault_Diagnosis_Technique_for_Wind_Energy_Conversion_Systems_Based_on_an_Improved_Particle_Swarm_Optimization/29116943CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/291169432022-09-07T03:00:00Z |
| spellingShingle | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization Majdi Mansouri (16869885) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning wind energy conversion (WEC) fault diagnosis particle swarm optimization (PSO) neural network (NN) euclidean distance (ED) |
| status_str | publishedVersion |
| title | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| title_full | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| title_fullStr | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| title_full_unstemmed | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| title_short | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| title_sort | An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning wind energy conversion (WEC) fault diagnosis particle swarm optimization (PSO) neural network (NN) euclidean distance (ED) |