Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems
<p dir="ltr">Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCP...
محفوظ في:
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2022
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| الموضوعات: | |
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| _version_ | 1864513548200181760 |
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| author | Amal Hichri (21383672) |
| author2 | Mansour Hajji (16869894) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author author author author |
| author_facet | Amal Hichri (21383672) Mansour Hajji (16869894) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Amal Hichri (21383672) Mansour Hajji (16869894) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2022-08-24T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/su141710518 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Genetic-Algorithm-Based_Neural_Network_for_Fault_Detection_and_Diagnosis_Application_to_Grid-Connected_Photovoltaic_Systems/29097023 |
| 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 Artificial intelligence Machine learning grid connected photovoltaic (GCPV) systems fault detection and diagnosis (FDD) artificial neural network (ANN) genetic algorithm (GA) feature selection (FS) |
| dc.title.none.fl_str_mv | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.</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/su141710518" target="_blank">https://dx.doi.org/10.3390/su141710518</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fccdb51d3b64335f05f839c5bb6782fd |
| identifier_str_mv | 10.3390/su141710518 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29097023 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic SystemsAmal Hichri (21383672)Mansour Hajji (16869894)Majdi Mansouri (16869885)Kamaleldin Abodayeh (16904865)Kais Bouzrara (16869906)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learninggrid connected photovoltaic (GCPV) systemsfault detection and diagnosis (FDD)artificial neural network (ANN)genetic algorithm (GA)feature selection (FS)<p dir="ltr">Modern photovoltaic (PV) systems have received significant attention regarding fault detection and diagnosis (FDD) for enhancing their operation by boosting their dependability, availability, and necessary safety. As a result, the problem of FDD in grid-connected PV (GCPV) systems is discussed in this work. Tools for feature extraction and selection and fault classification are applied in the developed FDD approach to monitor the GCPV system under various operating conditions. This is addressed such that the genetic algorithm (GA) technique is used for selecting the best features and the artificial neural network (ANN) classifier is applied for fault diagnosis. Only the most important features are selected to be supplied to the ANN classifier. The classification performance is determined via different metrics for various GA-based ANN classifiers using data extracted from the healthy and faulty data of the GCPV system. A thorough analysis of 16 faults applied on the module is performed. In general terms, the faults observed in the system are classified under three categories: simple, multiple, and mixed. The obtained results confirm the feasibility and effectiveness with a low computation time of the proposed approach for fault diagnosis.</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/su141710518" target="_blank">https://dx.doi.org/10.3390/su141710518</a></p>2022-08-24T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/su141710518https://figshare.com/articles/journal_contribution/Genetic-Algorithm-Based_Neural_Network_for_Fault_Detection_and_Diagnosis_Application_to_Grid-Connected_Photovoltaic_Systems/29097023CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290970232022-08-24T09:00:00Z |
| spellingShingle | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems Amal Hichri (21383672) Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning grid connected photovoltaic (GCPV) systems fault detection and diagnosis (FDD) artificial neural network (ANN) genetic algorithm (GA) feature selection (FS) |
| status_str | publishedVersion |
| title | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| title_full | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| title_fullStr | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| title_full_unstemmed | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| title_short | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| title_sort | Genetic-Algorithm-Based Neural Network for Fault Detection and Diagnosis: Application to Grid-Connected Photovoltaic Systems |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning grid connected photovoltaic (GCPV) systems fault detection and diagnosis (FDD) artificial neural network (ANN) genetic algorithm (GA) feature selection (FS) |