Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks-Reference-Cited by-同舟云学术

Methodology for automatic fault detection in photovoltaic arrays from artificial neural networks

Published:2021-01-01 Issue:1 Volume:8 Page:
ISSN:2331-1916
Container-title:Cogent Engineering
language:en
Short-container-title:Cogent Engineering

Author:

Colmenares-Quintero Ramón Fernando¹^ORCID,Rojas-Martinez Eyberth R.²,Macho-Hernantes Fernando³,Stansfield Kim E.⁴,Colmenares-Quintero Juan Carlos⁵^ORCID

Affiliation:

1. Faculty of Engineering, Universidad Cooperativa de Colombia, Medellín, Colombia

2. Faculty of Engineering, Universidad Cooperativa de Colombia, Santa Marta, Colombia

3. Servicios Empresariales Comunitarios, S. L. (SEC), Alameda de Recalde 36, Bilbao, Spain

4. University of Warwick, Coventry CV4 7AL, Warwick, UK

5. Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland

Funder

“Enhancing Aquatic Renewable Energy (ARE): Technology design and adaptation program for Colombia”

Newton Fund/Royal Academy of Engineering under the Industry Academy Partnership Programme

Publisher

Informa UK Limited

Subject

General Engineering,General Chemical Engineering,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/23311916.2021.1981520

Reference19 articles.

1. Optimum Efficiency of PV Panel Using Genetic Algorithms to Touch Proximate Zero Energy House (NZEH)

2. Neuro-Fuzzy Fault Detection Method for Photovoltaic Systems

3. Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions

4. A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

5. Fault detection method for grid-connected photovoltaic plants

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