Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image
Clustering or grouping is among the most important image processing methods that aim to split an image into different groups. Examining the literature, many clustering algorithms have been carried out, where the K-means algorithm is considered among the simplest and most used to classify an image in...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2020/6617597 |
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| author | Abdelilah Et-taleby Mohammed Boussetta Mohamed Benslimane |
| author_facet | Abdelilah Et-taleby Mohammed Boussetta Mohamed Benslimane |
| author_sort | Abdelilah Et-taleby |
| collection | DOAJ |
| description | Clustering or grouping is among the most important image processing methods that aim to split an image into different groups. Examining the literature, many clustering algorithms have been carried out, where the K-means algorithm is considered among the simplest and most used to classify an image into many regions. In this context, the main objective of this work is to detect and locate precisely the damaged area in photovoltaic (PV) fields based on the clustering of a thermal image through the K-means algorithm. The clustering quality depends on the number of clusters chosen; hence, the elbow, the average silhouette, and NbClust R package methods are used to find the optimal number K. The simulations carried out show that the use of the K-means algorithm allows detecting precisely the faults in PV panels. The excellent result is given with three clusters that is suggested by the elbow method. |
| format | Article |
| id | doaj-art-bc6c6f55577e4f7fbf258cbb869f711b |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-bc6c6f55577e4f7fbf258cbb869f711b2025-02-03T01:25:46ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2020-01-01202010.1155/2020/66175976617597Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal ImageAbdelilah Et-taleby0Mohammed Boussetta1Mohamed Benslimane2Innovative Technologies Laboratory Univérsité Sidi Mohamed Ben Abdellah Fez, MoroccoInnovative Technologies Laboratory Univérsité Sidi Mohamed Ben Abdellah Fez, MoroccoInnovative Technologies Laboratory Univérsité Sidi Mohamed Ben Abdellah Fez, MoroccoClustering or grouping is among the most important image processing methods that aim to split an image into different groups. Examining the literature, many clustering algorithms have been carried out, where the K-means algorithm is considered among the simplest and most used to classify an image into many regions. In this context, the main objective of this work is to detect and locate precisely the damaged area in photovoltaic (PV) fields based on the clustering of a thermal image through the K-means algorithm. The clustering quality depends on the number of clusters chosen; hence, the elbow, the average silhouette, and NbClust R package methods are used to find the optimal number K. The simulations carried out show that the use of the K-means algorithm allows detecting precisely the faults in PV panels. The excellent result is given with three clusters that is suggested by the elbow method.http://dx.doi.org/10.1155/2020/6617597 |
| spellingShingle | Abdelilah Et-taleby Mohammed Boussetta Mohamed Benslimane Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image International Journal of Photoenergy |
| title | Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image |
| title_full | Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image |
| title_fullStr | Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image |
| title_full_unstemmed | Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image |
| title_short | Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image |
| title_sort | faults detection for photovoltaic field based on k means elbow and average silhouette techniques through the segmentation of a thermal image |
| url | http://dx.doi.org/10.1155/2020/6617597 |
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