Fault detection in photovoltaic arrays via sparse representation classifier

Basit Öğe Kaydı
dc.contributor.authorKılıç, Heybet
dc.contributor.authorKhaki, Behnam
dc.contributor.authorGümüş, Bilal
dc.contributor.authorYılmaz, Musa
dc.contributor.authorPalensky, Peter
dc.contributor.orcid0000-0002-6119-0886
dc.date.accessioned2024-04-24T17:56:26Z
dc.date.available2024-04-24T17:56:26Z
dc.date.issued2020
dc.departmentDicle Üniversitesi, Diyarbakır Teknik Bilimler Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.descriptionIEEE;IEEE Industrial Electronics Society (IES)en_US
dc.description29th IEEE International Symposium on Industrial Electronics, ISIE 2020 -- 17 June 2020 through 19 June 2020 -- -- 162115en_US
dc.description.abstractIn recent years, there has been an increasing interest in the integration of photovoltaic (PV) systems in the power grids. Although PV systems provide the grid with clean and renewable energy, their unsafe and inefficient operation can affect the grid reliability. Early stage fault detection plays a crucial role in reducing the operation and maintenance costs and provides a long lifespan for PV arrays. PV Fault detection, however, is challenging especially when DC short circuit occurs under the low irradiance conditions while the arrays are equipped with an active maximum power point tracking (MPPT) mechanism. In this case, the efficiency and power output of a PV array decrease significantly under hard-to-detect faults such as active MPPT and low irradiance. If the hard-to-detect faults are not detected effectively, they will lead to PV array damage and potential fire hazard. To address this issue, in this paper we propose a new sparse representation classifier (SRC) based on feature extraction to effectively detect DC short circuit faults of PV array. To verify the effectiveness of the proposed SRC fault detection method, we use numerical simulation and compare its performance with the artificial neural network (ANN) based fault detection.en_US
dc.identifier.citationKılıç, H., Khaki, B., Gümüş, B., Yılmaz, M. ve Palensky, P. (2020). Fault detection in photovoltaic arrays via sparse representation slassifier. IEEE International Symposium on Industrial Electronics. 1015-1021.
dc.identifier.doi10.1109/ISIE45063.2020.9152421
dc.identifier.endpage1021en_US
dc.identifier.isbn9781728156354
dc.identifier.scopus2-s2.0-85089488085
dc.identifier.scopusqualityN/A
dc.identifier.startpage1015en_US
dc.identifier.urihttps://doi.org/10.1109/ISIE45063.2020.9152421
dc.identifier.urihttps://hdl.handle.net/11468/23513
dc.identifier.volume2020-Juneen_US
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE International Symposium on Industrial Electronics
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCompressive sensingen_US
dc.subjectPhotovoltaic array fault detectionen_US
dc.subjectSparse representation.en_US
dc.titleFault detection in photovoltaic arrays via sparse representation classifieren_US
dc.titleFault detection in photovoltaic arrays via sparse representation classifier
dc.typeConference Objecten_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
Fault Detection in Photovoltaic Arrays via Sparse Representation Classifier.pdf
Boyut:
488.25 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Konferans Öğesi
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu