Fault detection and diagnosis in grid-connected PV systems under irradiance variations
<p>Nowadays, photovoltaic (PV) energy is considered as one of the most encouraging renewable energy sources. Nevertheless, the power delivered by a PV field is strongly attached to irradiance which undergoes rapid variations depending on the climatic conditions. Accordingly, it becomes extreme...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2023
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| _version_ | 1864513529493585920 |
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| author | Mansour Hajji (16869894) |
| author2 | Zahra Yahyaoui (17821208) Majdi Mansouri (16869885) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author author |
| author_facet | Mansour Hajji (16869894) Zahra Yahyaoui (17821208) Majdi Mansouri (16869885) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mansour Hajji (16869894) Zahra Yahyaoui (17821208) Majdi Mansouri (16869885) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2023-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.egyr.2023.03.033 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Fault_detection_and_diagnosis_in_grid-connected_PV_systems_under_irradiance_variations/25036577 |
| 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 Irradiance variation (IV) Grid connected PV (GCPV) system Fault detection and diagnosis (FDD) |
| dc.title.none.fl_str_mv | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Nowadays, photovoltaic (PV) energy is considered as one of the most encouraging renewable energy sources. Nevertheless, the power delivered by a PV field is strongly attached to irradiance which undergoes rapid variations depending on the climatic conditions. Accordingly, it becomes extremely difficult to distinguish if it refers to faulty status in the system or healthy status under the irradiance variation (IV). Therefore, PV monitoring considering IV condition is fundamental in ensuring high reliability as well as improving power production of PV systems. In fault detection and diagnosis (FDD) field, researchers have considered the variation of irradiance (especially under low irradiance level) as faulty operating mode while others have considered it as fixed parameter during detecting faults. In this paper, therefore, firstly, the IV is introduced in the dynamic model of the grid connected PV (GCPV) system in different operating conditions. Then, an efficient and robust FDD approach based on machine learning and deep learning techniques is proposed in order to identify the healthy and faulty operating conditions. The obtained results through simulated data of a 12 kW PV module are extremely encouraging with a high accuracy under different studied cases.</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.2023.03.033" target="_blank">https://dx.doi.org/10.1016/j.egyr.2023.03.033</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_66216fec8fd19938673cdc33cb0a72f8 |
| identifier_str_mv | 10.1016/j.egyr.2023.03.033 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25036577 |
| publishDate | 2023 |
| 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 diagnosis in grid-connected PV systems under irradiance variationsMansour Hajji (16869894)Zahra Yahyaoui (17821208)Majdi Mansouri (16869885)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesMachine learningIrradiance variation (IV)Grid connected PV (GCPV) systemFault detection and diagnosis (FDD)<p>Nowadays, photovoltaic (PV) energy is considered as one of the most encouraging renewable energy sources. Nevertheless, the power delivered by a PV field is strongly attached to irradiance which undergoes rapid variations depending on the climatic conditions. Accordingly, it becomes extremely difficult to distinguish if it refers to faulty status in the system or healthy status under the irradiance variation (IV). Therefore, PV monitoring considering IV condition is fundamental in ensuring high reliability as well as improving power production of PV systems. In fault detection and diagnosis (FDD) field, researchers have considered the variation of irradiance (especially under low irradiance level) as faulty operating mode while others have considered it as fixed parameter during detecting faults. In this paper, therefore, firstly, the IV is introduced in the dynamic model of the grid connected PV (GCPV) system in different operating conditions. Then, an efficient and robust FDD approach based on machine learning and deep learning techniques is proposed in order to identify the healthy and faulty operating conditions. The obtained results through simulated data of a 12 kW PV module are extremely encouraging with a high accuracy under different studied cases.</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.2023.03.033" target="_blank">https://dx.doi.org/10.1016/j.egyr.2023.03.033</a></p>2023-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.egyr.2023.03.033https://figshare.com/articles/journal_contribution/Fault_detection_and_diagnosis_in_grid-connected_PV_systems_under_irradiance_variations/25036577CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/250365772023-12-01T00:00:00Z |
| spellingShingle | Fault detection and diagnosis in grid-connected PV systems under irradiance variations Mansour Hajji (16869894) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Irradiance variation (IV) Grid connected PV (GCPV) system Fault detection and diagnosis (FDD) |
| status_str | publishedVersion |
| title | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| title_full | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| title_fullStr | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| title_full_unstemmed | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| title_short | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| title_sort | Fault detection and diagnosis in grid-connected PV systems under irradiance variations |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Machine learning Irradiance variation (IV) Grid connected PV (GCPV) system Fault detection and diagnosis (FDD) |