Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image

Et-taleby, Abdelilah; Boussetta, Mohammed; Benslimane, Mohamed

doi:10.1155/2020/6617597

Cited by 51 publications

(40 citation statements)

References 22 publications

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“…However, this marginal variation between the Elbow method and the Silhouette method should not be understood as a universal law but rather as an application case due to the presence of particular data or as a result of a data-driven analytical procedure. However, there are cases in which the choice between the Elbow method and the Silhouette method generates results that are particularly divergent as indicated in [35]. Finally, from a strictly geographical point of view, it is possible to note that the countries that have most evolved from the point of view of Internet User Skills are the Scandinavian countries followed by the countries of central Europe such as Germany, Austria, Luxembourg, Estonia, Belgium.…”

Section: Clusterization With K-means Algorithm Optimized With Silhoue...mentioning

confidence: 99%

The Determinants of Internet User Skills in Europe

Leogrande,

Magaletti,

Cosoli

et al. 2022

SSRN Journal

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Section: Clusterization With K-means Algorithm Optimized With Silhoue...mentioning

confidence: 99%

The Determinants of Internet User Skills in Europe

Leogrande,

Magaletti,

Cosoli

et al. 2022

SSRN Journal

View full text Add to dashboard Cite

“…Many clustering techniques, such as K-means, entail an iterative learning procedure to improve coherence, thereby minimizing the distortion of a cluster [ 16 ]. For the data points inside a cluster, the distortion can be calculated using Sum Square Error (SSE) among each cluster’s points and its centroid [ 17 , 18 ]. SSE, in this case, represents the summation of distances

among data points (

, …,

) and the centroid cn as follows [ 16 ]:

…”

Section: Literature Reviewmentioning

confidence: 99%

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Fatlawi

Kiss

2023

Sensors

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Adaptive machine learning has increasing importance due to its ability to classify a data stream and handle the changes in the data distribution. Various resources, such as wearable sensors and medical devices, can generate a data stream with an imbalanced distribution of classes. Many popular oversampling techniques have been designed for imbalanced batch data rather than a continuous stream. This work proposes a self-adjusting window to improve the adaptive classification of an imbalanced data stream based on minimizing cluster distortion. It includes two models; the first chooses only the previous data instances that preserve the coherence of the current chunk’s samples. The second model relaxes the strict filter by excluding the examples of the last chunk. Both models include generating synthetic points for oversampling rather than the actual data points. The evaluation of the proposed models using the Siena EEG dataset showed their ability to improve the performance of several adaptive classifiers. The best results have been obtained using Adaptive Random Forest in which Sensitivity reached 96.83% and Precision reached 99.96%.

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“…For clustering, the most popular method is k-means clustering, which is an unsupervised ML algorithm. This method consists of dividing data in k clusters that will be grouped by the mean distance between points [45,46].…”

Section: Other Machine Learning Techniquesmentioning

confidence: 99%

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

2022

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In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

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Faults Detection for Photovoltaic Field Based on K-Means, Elbow, and Average Silhouette Techniques through the Segmentation of a Thermal Image

Cited by 51 publications

References 22 publications

The Determinants of Internet User Skills in Europe

The Determinants of Internet User Skills in Europe

An Elastic Self-Adjusting Technique for Rare-Class Synthetic Oversampling Based on Cluster Distortion Minimization in Data Stream

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

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