Hussain, Muhammad, Dhimish, Mahmoud, Titarenko, Sofya et al. (1 more author) (2020) Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters. Renewable Energy. pp. 1272-1292. ISSN 0960-1481
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier B.V. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 02 Sep 2021 14:50 |
Last Modified: | 03 Dec 2024 01:14 |
Published Version: | https://doi.org/10.1016/j.renene.2020.04.023 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.renene.2020.04.023 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177732 |