A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
<p>This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2021
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| الموضوعات: | |
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| _version_ | 1864513562738688000 |
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| author | Khaled Dhibi (16891524) |
| author2 | Radhia Fezai (16869888) Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author author author author |
| author_facet | Khaled Dhibi (16891524) Radhia Fezai (16869888) Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Khaled Dhibi (16891524) Radhia Fezai (16869888) Majdi Mansouri (16869885) Mohamed Trabelsi (16869891) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2021-04-21T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3074784 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_Hybrid_Fault_Detection_and_Diagnosis_of_Grid-Tied_PV_Systems_Enhanced_Random_Forest_Classifier_Using_Data_Reduction_and_Interval-Valued_Representation/24042486 |
| 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 Data management and data science Machine learning Circuit faults Data mining Fault diagnosis Feature extraction and selection Feature extraction Fault classification Interval-valued data Kernel PV systems Principal component analysis Random forest Radio frequency Reduced kernel principal component analysis Support vector machines |
| dc.title.none.fl_str_mv | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. The failures encompassed in this study are the open-circuit/short-circuit, islanding, output current sensor, and partial shading faults. In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. The main contribution of the proposed approach is to provide a good trade-off between low computation time and high classification metrics. The performance of the proposed IRKPCA-RF approach is assessed using a set of emulated data of a grid-tied PV system operating under healthy and faulty conditions. The presented results show that the proposed IRKPCA-RF approach is characterized by enhanced diagnosis metrics, classification rate, and computation time compared to the classical techniques.</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.2021.3074784" target="_blank">https://dx.doi.org/10.1109/access.2021.3074784</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_284dc7cd56a2c16c117ff8049b60b9fc |
| identifier_str_mv | 10.1109/access.2021.3074784 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24042486 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued RepresentationKhaled Dhibi (16891524)Radhia Fezai (16869888)Majdi Mansouri (16869885)Mohamed Trabelsi (16869891)Kais Bouzrara (16869906)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringInformation and computing sciencesData management and data scienceMachine learningCircuit faultsData miningFault diagnosisFeature extraction and selectionFeature extractionFault classificationInterval-valued dataKernelPV systemsPrincipal component analysisRandom forestRadio frequencyReduced kernel principal component analysisSupport vector machines<p>This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valued data representation, and with large-scale systems by using a dataset size-reduction framework. The failures encompassed in this study are the open-circuit/short-circuit, islanding, output current sensor, and partial shading faults. In the proposed FDD approach, named interval reduced kernel PCA (IRKPCA)-based Random Forest (IRKPCA-RF), the feature extraction and selection phase is performed using the IRKPCA models while the fault classification is ensured using the RF algorithm. The main contribution of the proposed approach is to provide a good trade-off between low computation time and high classification metrics. The performance of the proposed IRKPCA-RF approach is assessed using a set of emulated data of a grid-tied PV system operating under healthy and faulty conditions. The presented results show that the proposed IRKPCA-RF approach is characterized by enhanced diagnosis metrics, classification rate, and computation time compared to the classical techniques.</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.2021.3074784" target="_blank">https://dx.doi.org/10.1109/access.2021.3074784</a></p>2021-04-21T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3074784https://figshare.com/articles/journal_contribution/A_Hybrid_Fault_Detection_and_Diagnosis_of_Grid-Tied_PV_Systems_Enhanced_Random_Forest_Classifier_Using_Data_Reduction_and_Interval-Valued_Representation/24042486CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240424862021-04-21T00:00:00Z |
| spellingShingle | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation Khaled Dhibi (16891524) Engineering Electrical engineering Information and computing sciences Data management and data science Machine learning Circuit faults Data mining Fault diagnosis Feature extraction and selection Feature extraction Fault classification Interval-valued data Kernel PV systems Principal component analysis Random forest Radio frequency Reduced kernel principal component analysis Support vector machines |
| status_str | publishedVersion |
| title | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| title_full | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| title_fullStr | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| title_full_unstemmed | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| title_short | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| title_sort | A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation |
| topic | Engineering Electrical engineering Information and computing sciences Data management and data science Machine learning Circuit faults Data mining Fault diagnosis Feature extraction and selection Feature extraction Fault classification Interval-valued data Kernel PV systems Principal component analysis Random forest Radio frequency Reduced kernel principal component analysis Support vector machines |