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.

Evaluation of visible contamination on power grid insulators using convolutional neural networks

Frizzo Stefenon S;
2023-01-01

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1254471
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