Photovoltaic Array Fault Detection and Classification based on T-Distributed Stochastic Neighbor Embedding and Robust Soft Learning Vector Quantization

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2021

Autores

Afrasiabi, S (Autor) Outra; Afrasiabi, M (Autor) Outra; Behdani, B (Autor) Outra; Mohammadi, M (Autor) Outra; Javadi, MS (Autor) Outra; Osorio, GJ (Autor) Outra; Catalao, JPS (Autor) FEUP

Ata de Conferência Internacional

Título: 2021 21ST IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2021 5TH IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC/I&CPS EUROPE)

21st IEEE International Conference on Environment and Electrical Engineering / 5th IEEE Industrial and Commercial Power Systems Europe (EEEIC/I and CPS Europe)

Politecnico Bari, Bari, ITALY, SEP 07-10, 2021

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00W-BDT

DOI: 10.1109/eeeic/icpseurope51590.2021.9584770

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5