Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3919
Title: Photovoltaic array fault detection and classification based on t-distributed stochastic neighbor embedding and robust soft learning vector quantization
Authors: Afrasiabi, S.
Afrasiabi, M.
Behdani, B.
Mohammadi, M.
Javadi, Mohammad
Osório, Gerardo J.
Catalão, João P. S.
Keywords: Fault detection and classification
Photovoltaic
Robust soft learning vector quantization (RSLVQ)
T-distributed stochastic neighbor embedding (t-SNE)
Issue Date: Sep-2021
Publisher: IEEE
Citation: Afrasiabi, 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/11328/3919
Abstract: Photovoltaic (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.
URI: http://hdl.handle.net/11328/3919
ISBN: 978-1-6654-3613-7
Appears in Collections:REMIT - Publicações em Livros de Atas Internacionais / Papers in International Proceedings

Files in This Item:
File Description SizeFormat 
9584770.pdf412.84 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.