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

Cogent Engineering

Ramón Fernando Colmenares-Quintero¹  Eyberth R. Rojas-Martinez²  Juan Carlos Colmenares-Quintero³  Fernando Macho-Hernantes⁴  Kim E. Stansfield⁵ 

[1] Faculty of Engineering, Universidad Cooperativa de Colombia, Medellín, Colombi;Faculty of Engineering, Universidad Cooperativa de Colombia, Santa Marta, Colombi;Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Polan;Servicios Empresariales Comunitarios, S. L. (SEC), Alameda de Recalde 36, Bilbao, Spai;University of Warwick, Coventry CV4 7AL, Warwick, U;

关键词: Photovoltaic system;    fault detection;    Artificial Neural Network (ANN);    classification;    sustainable development goals (SDG).;   

DOI  :  10.1080/23311916.2021.1981520

来源: Taylor & Francis

【 摘 要 】

This article presents a methodology for automatic fault detection in photovoltaic arrays. Due to the great importance in the construction of increasingly robust photovoltaic plants, automatic fault detection has become a necessary tool to extend the useful life of these plants, avoid system shutdowns and reduce serious safety problems. In the present study, nine possible faults are detected, caused by malfunction in the bypass and blocking diodes. The solution consists of training two models based on artificial neural networks, the first model is a binary classifier that detects whether or not a fault occurs, the second is a multiclass classifier that detects the fault type. The obtained models were trained from simulation data, in an architecture of 9 photovoltaic panels interconnected in three rows by three columns matrix (extendable to larger systems). The evaluation shows that the prediction system has a total accuracy of 92.95%. Finally, this methodology is intended to be implemented in Colombia, in zones with difficult access and not interconnected to the electricity grid, seeking to reduce corrective maintenance.

【 授权许可】

CC BY