Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects
<p>Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the ef...
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2021
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| _version_ | 1864513562770145280 |
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| author | Majdi Mansouri (16869885) |
| author2 | Mohamed Trabelsi (16869891) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author |
| author_facet | Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2021-09-07T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3110947 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_Learning-Based_Fault_Diagnosis_of_Photovoltaic_Systems_A_Comprehensive_Review_and_Enhancement_Prospects/24038943 |
| 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 Circuit faults Deep learning Data models Fault diagnosis Feature extraction Iron Photovoltaic systems Tools |
| dc.title.none.fl_str_mv | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DL-based FDD for PV systems and provide guidelines for future research in the field.</p> <h2>Other Information</h2> <p>Published in: IEEE Access<br> License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3110947" target="_blank">https://dx.doi.org/10.1109/access.2021.3110947</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_15777e75894b5be6559755bdff95c018 |
| identifier_str_mv | 10.1109/access.2021.3110947 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24038943 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement ProspectsMajdi Mansouri (16869885)Mohamed Trabelsi (16869891)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceMachine learningCircuit faultsDeep learningData modelsFault diagnosisFeature extractionIronPhotovoltaic systemsTools<p>Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DL-based FDD for PV systems and provide guidelines for future research in the field.</p> <h2>Other Information</h2> <p>Published in: IEEE Access<br> License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br> See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3110947" target="_blank">https://dx.doi.org/10.1109/access.2021.3110947</a></p>2021-09-07T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3110947https://figshare.com/articles/journal_contribution/Deep_Learning-Based_Fault_Diagnosis_of_Photovoltaic_Systems_A_Comprehensive_Review_and_Enhancement_Prospects/24038943CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240389432021-09-07T00:00:00Z |
| spellingShingle | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects Majdi Mansouri (16869885) Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Circuit faults Deep learning Data models Fault diagnosis Feature extraction Iron Photovoltaic systems Tools |
| status_str | publishedVersion |
| title | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| title_full | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| title_fullStr | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| title_full_unstemmed | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| title_short | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| title_sort | Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects |
| topic | Engineering Electrical engineering Information and computing sciences Artificial intelligence Machine learning Circuit faults Deep learning Data models Fault diagnosis Feature extraction Iron Photovoltaic systems Tools |