Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

Muhammed Hussain, Mahmoud Dhimish, Sofya Titarenko and Peter Mather

Renewable Energy, 2020, vol. 155, issue C, 1272-1292

Abstract: In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Numerous literatures can be found on the topic of PV fault detection through the implementation of artificial intelligence. The novel part of this research is the successful development, deployment and validation of a fault detection PV system using radial basis function (RBF), requiring only two parameters as the input to the ANN (solar irradiance and output power). The results obtained through the testing of the developed ANN on a PV installation of 2.2 kW capacity, provided an accuracy of 97.9%. To endorse the accuracy of the newly developed algorithm, the ANN was tested on another PV system, installed at a remote location. The total capacity of the new system was significantly higher, 4.16 kW. A vital part of the test was to see how the proposed ANN would perform with ‘scaled-up’ input data, during normal operation as well as partial shading scenarios. The validation process provided an overall fault detection accuracy of above 97%. The decrease in accuracy was due to the varying nature of the two systems in terms of total capacity, number of samples and type of faults.

Keywords: Photovoltaics; Fault detection; Artificial intelligence; RBF Network (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:155:y:2020:i:c:p:1272-1292

DOI: 10.1016/j.renene.2020.04.023

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