Photovoltaic array fault detection and classification based on t-distributed stochastic neighbor embedding and robust soft learning vector quantization

dc.contributor.authorAfrasiabi, S.
dc.contributor.authorAfrasiabi, M.
dc.contributor.authorBehdani, B.
dc.contributor.authorMohammadi, M.
dc.contributor.authorJavadi, Mohammad
dc.contributor.authorCatalão, João P. S.
dc.contributor.authorOsório, Gerardo J.
dc.date.accessioned2022-02-01T12:39:13Z
dc.date.available2022-02-01T12:39:13Z
dc.date.issued2021-09
dc.description.abstractPhotovoltaic (PV) as one of the most promising energy alternatives brings a set of serious challenges in the operation of the power systems including PV system protection. Accordingly, it has become even more vital to provide reliable protection for the PV generations. To this end, this paper proposes two-stage data-driven methods. In the first stage, a feature selection method, namely t-distributed stochastic neighbor embedding (t-SNE) is implemented to select the optimal features. Then, the output of t-SNE is directly fed into the strong data-driven classification algorithm, namely robust soft learning vector quantization (RSLVQ) to detect PV array fault and identify the fault types in the second stage. The proposed method is able to detect the two different line-to-line faults (in strings and out of strings) and open circuit fault and fault type considering partial shedding effects. The results have been discussed based on simulation results and have been demonstrated the high accuracy and reliability of the proposed two-stage method in detection and fault type identification based on confusion matrix values.pt_PT
dc.identifier.citationAfrasiabi, S., Afrasiabi, M., Behdani, B., Mohammadi, M., Javadi, M., Osório, G. J., & Catalão, J. P. S. (2021). Photovoltaic array fault detection and classification based on t-distributed stochastic neighbor embedding and robust soft learning vector quantization. In Proceedings of the 21th IEEE International Conference on Environment and Electrical Engineering and 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC 2021 / I&CPS Europe 2021), Bari, Italy, 7-10 September 2021 (pp. 1-5). doi: 10.1109/EEEIC/ICPSEurope51590.2021.9584770. Disponível no Repositório UPT, http://hdl.handle.net/11328/3919pt_PT
dc.identifier.doi10.1109/EEEIC/ICPSEurope51590.2021.9584770pt_PT
dc.identifier.isbn978-1-6654-3613-7
dc.identifier.urihttp://hdl.handle.net/11328/3919
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherIEEEpt_PT
dc.relation.publisherversionhttps://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584770pt_PT
dc.rightsrestricted accesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectFault detection and classificationpt_PT
dc.subjectPhotovoltaicpt_PT
dc.subjectRobust soft learning vector quantization (RSLVQ)pt_PT
dc.subjectT-distributed stochastic neighbor embedding (t-SNE)pt_PT
dc.titlePhotovoltaic array fault detection and classification based on t-distributed stochastic neighbor embedding and robust soft learning vector quantizationpt_PT
dc.typejournal articlept_PT
degois.publication.issue5pt_PT
degois.publication.lastPage1pt_PT
degois.publication.locationBari, Italy, 7-10 September, 2021pt_PT
degois.publication.titleProceedings of the 21th IEEE International Conference on Environment and Electrical Engineering and 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC 2021 / I&CPS Europe 2021)pt_PT
dspace.entity.typePublicationen
person.affiliation.nameREMIT – Research on Economics, Management and Information Technologies
person.familyNameOsório
person.givenNameGerardo J.
person.identifier.ciencia-idBD19-D0AD-65DB
person.identifier.gsidt13DoaMAAAAJ
person.identifier.orcid0000-0001-8328-9708
person.identifier.ridC-3616-2014
person.identifier.scopus-author-id54783251300
relation.isAuthorOfPublication7ce5da40-610d-4361-a87f-5cbdfe392256
relation.isAuthorOfPublication.latestForDiscovery7ce5da40-610d-4361-a87f-5cbdfe392256

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