Vieira, Romênia G., Dhimish, Mahmoud, M. U. de Araújo, Fábio et al. (1 more author) (2022) Comparing multilayer perceptron and probabilistic neural network for PV systems fault detection. Expert systems with applications. 117248. ISSN 0957-4174
Abstract
This work introduces the development of a fault detection method for photovoltaic (PV) systems using artificial neural networks (ANN). The faults identified by the method are short-circuited modules and disconnected strings. This research's novel part is its adaptability as a long-term dataset has been used in the ANN training and validation phase and also examined situations considering datasets contaminated with random noise. It makes the method suitable for any photovoltaic power plant, also does not require long datasets from pre-existing systems or installing new sensors. The proposed method comprises two unique algorithms for PV fault detection, a Multilayer Perceptron, and a Probabilistic Neural Network. The research method used modeling, simulation, and experiment data since both algorithms were trained using simulated datasets and tested through experimental data from two different photovoltaic systems. Even though the training dataset includes noisy situations, the results indicated a superior precision for the Multilayer Perceptron neural network. The findings showed a maximum accuracy of 99.1% in detecting short-circuited modules and 100% in detecting disconnected strings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. |
Keywords: | Renewable Energy,Photovoltaic,Fault Detection,Artificial Intelligence,Machine Learning |
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: | 20 Apr 2022 14:30 |
Last Modified: | 02 Apr 2025 23:24 |
Published Version: | https://doi.org/10.1016/j.eswa.2022.117248 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/j.eswa.2022.117248 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185918 |
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Description: Comparing Multilayer Perceptron and Probabilistic Neural Network for PV Systems Fault Detection
Licence: CC-BY-NC-ND 2.5