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14.07.2023 | Original Paper

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

verfasst von: Marcelo Picolotto Corso, Stefano Frizzo Stefenon, Gurmail Singh, Marcos Vinicius Matsuo, Fábio Luis Perez, Valderi Reis Quietinho Leithardt

Erschienen in: Electrical Engineering | Ausgabe 6/2023

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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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Titel: Evaluation of visible contamination on power grid insulators using convolutional neural networks
verfasst von: Marcelo Picolotto Corso
Stefano Frizzo Stefenon
Gurmail Singh
Marcos Vinicius Matsuo
Fábio Luis Perez
Valderi Reis Quietinho Leithardt
Publikationsdatum: 14.07.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Electrical Engineering / Ausgabe 6/2023
Print ISSN: 0948-7921
Elektronische ISSN: 1432-0487
DOI: https://doi.org/10.1007/s00202-023-01915-2

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