Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems
<p>One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learning technique aims to develop effective models that c...
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2022
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| _version_ | 1864513562695696384 |
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| author | Khaled Dhibi (16891524) |
| author2 | Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author2_role | author author author author author |
| author_facet | Khaled Dhibi (16891524) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| author_role | author |
| dc.creator.none.fl_str_mv | Khaled Dhibi (16891524) Majdi Mansouri (16869885) Kamaleldin Abodayeh (16904865) Kais Bouzrara (16869906) Hazem Nounou (16869900) Mohamed Nounou (3489386) |
| dc.date.none.fl_str_mv | 2022-04-13T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3167147 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Interval-Valued_Reduced_Ensemble_Learning_Based_Fault_Detection_and_Diagnosis_Techniques_for_Uncertain_Grid-Connected_PV_Systems/24056445 |
| 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 Distributed computing and systems software Machine learning Circuit faults Prediction algorithms Support vector machines Proposals Neural networks Feature extraction Fault detection Uncertain systems Ensemble learning Fault diagnosis Interval-valued data Kernel principal component analysis (KPCA) Grid-connected PV (GCPV) |
| dc.title.none.fl_str_mv | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learning technique aims to develop effective models that can provide a better rate of accuracy. Recently, numerous machine learning based ensemble models have been applied in FDD using different combination techniques. Ensemble method is a tool that merges several base models in order to produce one optimal predictive model. In this study, we propose six effective Ensemble Leaning (EL)-based FDD paradigms for uncertain Grid-Connected PV systems. First, EL-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with PV system uncertainties (current/voltage variability, noise, measurement errors, … ). Next, in order to more improve the diagnosis abilities, two interval kernel PCA (IKPCA)-based EL classifiers are developed. The IKPCA-EL techniques are addressed so that the features extraction and selection phases are performed using the IKPCA models and the sensitive and significant interval-valued characteristics are transmitted to the EL model for classification purposes. Finally, the number of observations in the training data set is reduced using Hierarchical K-means techniques in order to overcome the problem of computation time and storage cost. Therefore, two interval reduced KPCA-EL techniques are proposed. The study demonstrated the feasibility and efficiency of the proposed techniques for fault diagnosis of Grid-Connected PV systems.</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.2022.3167147" target="_blank">https://dx.doi.org/10.1109/access.2022.3167147</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_1796eaad1870d9b7a31abe967b4cb178 |
| identifier_str_mv | 10.1109/access.2022.3167147 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056445 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV SystemsKhaled Dhibi (16891524)Majdi Mansouri (16869885)Kamaleldin Abodayeh (16904865)Kais Bouzrara (16869906)Hazem Nounou (16869900)Mohamed Nounou (3489386)EngineeringElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesDistributed computing and systems softwareMachine learningCircuit faultsPrediction algorithmsSupport vector machinesProposalsNeural networksFeature extractionFault detectionUncertain systemsEnsemble learningFault diagnosisInterval-valued dataKernel principal component analysis (KPCA)Grid-connected PV (GCPV)<p>One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learning technique aims to develop effective models that can provide a better rate of accuracy. Recently, numerous machine learning based ensemble models have been applied in FDD using different combination techniques. Ensemble method is a tool that merges several base models in order to produce one optimal predictive model. In this study, we propose six effective Ensemble Leaning (EL)-based FDD paradigms for uncertain Grid-Connected PV systems. First, EL-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with PV system uncertainties (current/voltage variability, noise, measurement errors, … ). Next, in order to more improve the diagnosis abilities, two interval kernel PCA (IKPCA)-based EL classifiers are developed. The IKPCA-EL techniques are addressed so that the features extraction and selection phases are performed using the IKPCA models and the sensitive and significant interval-valued characteristics are transmitted to the EL model for classification purposes. Finally, the number of observations in the training data set is reduced using Hierarchical K-means techniques in order to overcome the problem of computation time and storage cost. Therefore, two interval reduced KPCA-EL techniques are proposed. The study demonstrated the feasibility and efficiency of the proposed techniques for fault diagnosis of Grid-Connected PV systems.</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.2022.3167147" target="_blank">https://dx.doi.org/10.1109/access.2022.3167147</a></p>2022-04-13T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3167147https://figshare.com/articles/journal_contribution/Interval-Valued_Reduced_Ensemble_Learning_Based_Fault_Detection_and_Diagnosis_Techniques_for_Uncertain_Grid-Connected_PV_Systems/24056445CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240564452022-04-13T00:00:00Z |
| spellingShingle | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems Khaled Dhibi (16891524) Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Distributed computing and systems software Machine learning Circuit faults Prediction algorithms Support vector machines Proposals Neural networks Feature extraction Fault detection Uncertain systems Ensemble learning Fault diagnosis Interval-valued data Kernel principal component analysis (KPCA) Grid-connected PV (GCPV) |
| status_str | publishedVersion |
| title | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| title_full | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| title_fullStr | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| title_full_unstemmed | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| title_short | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| title_sort | Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Distributed computing and systems software Machine learning Circuit faults Prediction algorithms Support vector machines Proposals Neural networks Feature extraction Fault detection Uncertain systems Ensemble learning Fault diagnosis Interval-valued data Kernel principal component analysis (KPCA) Grid-connected PV (GCPV) |