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Evaluation of visible contamination on power grid insulators using convolutional neural networks.
- Source :
-
Electrical Engineering . Dec2023, Vol. 105 Issue 6, p3881-3894. 14p. - Publication Year :
- 2023
-
Abstract
- The contamination of insulators increases their surface conductivity, resulting in a higher chance of shutdowns occurring. To measure contamination, equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) are used. In this paper, the VGG-11, VGG-13, VGG-16, VGG-19, ResNet-18, ResNet-34, ResNet-50, ResNet-152, DenseNet-121, DenseNet-161, DenseNet-169, and DenseNet-201 convolutional neural networks (CNNs) were considered to classify the visible contamination of pin-type distribution power grid insulators. The NSDD presents more visual variation than ESDD when artificial contamination is evaluated. Comparing the CNNs, the ResNet-50 had the best performance for classifying visible contamination using unbalanced data with an accuracy of 99.242% and an F1-score of 0.97436, respectively. In benchmarking, the ResNet-50 outperformed well-established classifiers such as the multilayer perceptron, support vector machine, k-nearest neighbors, decision tree, ensemble bagged trees, and quadratic discriminant. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09487921
- Volume :
- 105
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Electrical Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 173273364
- Full Text :
- https://doi.org/10.1007/s00202-023-01915-2