Deployment of AI-based RBF network for photovoltaics fault detection procedure
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,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2020-05-01
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| Series: | AIMS Electronics and Electrical Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/10.3934/ElectrEng.2020.1.1/fulltext.html |
| _version_ | 1818896743587119104 |
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| author | Muhammad Hussain Mahmoud Dhimish Violeta Holmes Peter Mather |
| author_facet | Muhammad Hussain Mahmoud Dhimish Violeta Holmes Peter Mather |
| author_sort | Muhammad Hussain |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T19:05:08Z |
| format | Article |
| id | doaj.art-4531764b4c2d40f2a7ab7a26c8599bf6 |
| institution | Directory Open Access Journal |
| issn | 2578-1588 |
| language | English |
| last_indexed | 2024-12-19T19:05:08Z |
| publishDate | 2020-05-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Electronics and Electrical Engineering |
| spelling | doaj.art-4531764b4c2d40f2a7ab7a26c8599bf62022-12-21T20:09:27ZengAIMS PressAIMS Electronics and Electrical Engineering2578-15882020-05-014111810.3934/ElectrEng.2020.1.1Deployment of AI-based RBF network for photovoltaics fault detection procedureMuhammad Hussain0 Mahmoud Dhimish1Violeta Holmes2Peter Mather3Department of Engineering and Technology, Laboratory of Photovoltaics, University of Huddersfield, Huddersfield, HD1 3DH, United KingdomDepartment of Engineering and Technology, Laboratory of Photovoltaics, University of Huddersfield, Huddersfield, HD1 3DH, United KingdomDepartment of Engineering and Technology, Laboratory of Photovoltaics, University of Huddersfield, Huddersfield, HD1 3DH, United KingdomDepartment of Engineering and Technology, Laboratory of Photovoltaics, University of Huddersfield, Huddersfield, HD1 3DH, United KingdomIn 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.https://www.aimspress.com/article/10.3934/ElectrEng.2020.1.1/fulltext.htmlrenewable energyphotovoltaicsfault detectionartificial intelligence |
| spellingShingle | Muhammad Hussain Mahmoud Dhimish Violeta Holmes Peter Mather Deployment of AI-based RBF network for photovoltaics fault detection procedure AIMS Electronics and Electrical Engineering renewable energy photovoltaics fault detection artificial intelligence |
| title | Deployment of AI-based RBF network for photovoltaics fault detection procedure |
| title_full | Deployment of AI-based RBF network for photovoltaics fault detection procedure |
| title_fullStr | Deployment of AI-based RBF network for photovoltaics fault detection procedure |
| title_full_unstemmed | Deployment of AI-based RBF network for photovoltaics fault detection procedure |
| title_short | Deployment of AI-based RBF network for photovoltaics fault detection procedure |
| title_sort | deployment of ai based rbf network for photovoltaics fault detection procedure |
| topic | renewable energy photovoltaics fault detection artificial intelligence |
| url | https://www.aimspress.com/article/10.3934/ElectrEng.2020.1.1/fulltext.html |
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