Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters. (August 2020)

• Record Type: Journal Article
• Authors: Hussain, Muhammed; Dhimish, Mahmoud; Titarenko, Sofya; Mather, Peter
• Is Part Of: Renewable energy. Volume 155(2020)
• Page Start: 1272
• Page End: 1292
• Publication Date: 2020-08
• Subjects: Photovoltaics -- Fault detection -- Artificial intelligence -- RBF Network; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.04.023
• Languages: English
• ISSNs: 0960-1481

Abstract

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. Highlights: ANN-based photovoltaic (PV) detection algorithm is presented. Only two input parameters were used for the ANN, solar irradiance and PV output power. Four different ANN-based methodologies were examined and practically testified. Overall ANN fault detecting accuracy is in the range of 96.5%–98.1%.