Fault detection in photovoltaic arrays via sparse representation classifier

Basit Öğe Kaydı
dc.authorid0000-0003-4665-5339en_US
dc.contributor.authorKılıç, Heybet
dc.contributor.authorKhaki, Behnam
dc.contributor.authorGümüş, Bilal
dc.contributor.authorYılmaz, Musa
dc.contributor.authorPalensky, Peter
dc.date.accessioned2022-05-30T13:22:52Z
dc.date.available2022-05-30T13:22:52Z
dc.date.issued2020en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionWOS:000612836800166
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 classifier. 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE). New York: IEEE.en_US
dc.identifier.endpage7en_US
dc.identifier.isbn978-1-7281-5635-4
dc.identifier.issn2163-5137
dc.identifier.startpage1en_US
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9152421
dc.identifier.urihttps://hdl.handle.net/11468/9916
dc.identifier.wosWOS:000612836800166
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.institutionauthorGümüş, Bilal
dc.language.isoenen_US
dc.publisherIEEE-Institute of Electrical Electronics Engineers INC.en_US
dc.relation.ispartof2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)
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 representationen_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
Küçük Resim
İsim:
Fault Detection in Photovoltaic Arrays via Sparse Representation Classifier.pdf
Boyut:
606.31 KB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Elektrik Elektronik Mühendisliği Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu