Hussain, Muhammad, Dhimish, Mahmoud, Holmes, Violeta et al. (1 more author) (2019) Deployment of AI-based RBF network for photovoltaics fault detection procedure. AIMS Electronics and Electrical Engineering. pp. 1-18. ISSN 2578-1588
Abstract
In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Although, a rich amount of research is available in the field of PV fault detection using ANN, this paper presents a novel methodology based on only two inputs for the training, validating and testing of the Radial Basis Function (RBF) network achieving unprecedented detection accuracy of 98.1%. The proposed methodology goes beyond data normalisation and implements a ‘mapping of inputs’ approach to the data set before exposing it to the network for training. The accuracy of the proposed network is further endorsed through testing of the network in partial shading and overcast conditions.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
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 14:00 |
Last Modified: | 20 Feb 2025 00:09 |
Published Version: | https://doi.org/10.3934/ElectrEng.2020.1.1 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3934/ElectrEng.2020.1.1 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177718 |