Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network

This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollu...

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Autores principales:	B. Vigneshwaran, R.V. Maheswari, L. Kalaivani, Vimal Shanmuganathan, Seungmin Rho, Seifedine Kadry, Mi Young Lee
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Dual-input CNN Convolution Neural Network Bayesian optimization Feature fusion Training optimizer High voltage insulator Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/db1f6ec7a0df4bef991a0e487ca9daf5
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spelling	oai:doaj.org-article:db1f6ec7a0df4bef991a0e487ca9daf52021-11-28T04:33:32ZRecognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network2352-484710.1016/j.egyr.2020.12.044https://doaj.org/article/db1f6ec7a0df4bef991a0e487ca9daf52021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721000019https://doaj.org/toc/2352-4847This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollution layer artificially created for HV insulator with three straight shed ball end fitting in a laboratory setup and corresponding PD readings are measured. The wavelet transform is employed to represent the measured PD signal as scalogram patterns. In general CNN uses a single input pattern for feature extraction. If the pattern quality is low, it is easy to cause misclassification. Hence in this proposed work, the feature fusion of a dual-input Visual Geometry Group (VGG) based CNN is used for the classification of contamination layer. VGG 19 is a pretrained deep learning network used for extracting the rich features from the patterns. In continuation to that, hyperparameter (HP) play a vital role in deep learning algorithms because they directly manage the behaviours of training algorithms and have a significant effect on the performance of deep learning models. Hence, Bayesian Optimization (BO) is used for tuning the HP. At last, to check the practicality of the proposed algorithm, a new dataset is created for 11 kV polymer insulator with three alternate shed clevis end fitting and different pollution levels—acceptable results obtained by using dual-input CNN with the minimum quantity of data.B. VigneshwaranR.V. MaheswariL. KalaivaniVimal ShanmuganathanSeungmin RhoSeifedine KadryMi Young LeeElsevierarticleDual-input CNNConvolution Neural NetworkBayesian optimizationFeature fusionTraining optimizerHigh voltage insulatorElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 7878-7889 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dual-input CNN Convolution Neural Network Bayesian optimization Feature fusion Training optimizer High voltage insulator Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Dual-input CNN Convolution Neural Network Bayesian optimization Feature fusion Training optimizer High voltage insulator Electrical engineering. Electronics. Nuclear engineering TK1-9971 B. Vigneshwaran R.V. Maheswari L. Kalaivani Vimal Shanmuganathan Seungmin Rho Seifedine Kadry Mi Young Lee Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
description	This paper portrays the application of a Partial Discharge (PD) signal combined with the dual-input VGG Convolution Neural Network (CNN) to predict the location of the pollution layer on 11 kV polymer insulators subjected to alternating current for smart grid applications. First, a non-uniform pollution layer artificially created for HV insulator with three straight shed ball end fitting in a laboratory setup and corresponding PD readings are measured. The wavelet transform is employed to represent the measured PD signal as scalogram patterns. In general CNN uses a single input pattern for feature extraction. If the pattern quality is low, it is easy to cause misclassification. Hence in this proposed work, the feature fusion of a dual-input Visual Geometry Group (VGG) based CNN is used for the classification of contamination layer. VGG 19 is a pretrained deep learning network used for extracting the rich features from the patterns. In continuation to that, hyperparameter (HP) play a vital role in deep learning algorithms because they directly manage the behaviours of training algorithms and have a significant effect on the performance of deep learning models. Hence, Bayesian Optimization (BO) is used for tuning the HP. At last, to check the practicality of the proposed algorithm, a new dataset is created for 11 kV polymer insulator with three alternate shed clevis end fitting and different pollution levels—acceptable results obtained by using dual-input CNN with the minimum quantity of data.
format	article
author	B. Vigneshwaran R.V. Maheswari L. Kalaivani Vimal Shanmuganathan Seungmin Rho Seifedine Kadry Mi Young Lee
author_facet	B. Vigneshwaran R.V. Maheswari L. Kalaivani Vimal Shanmuganathan Seungmin Rho Seifedine Kadry Mi Young Lee
author_sort	B. Vigneshwaran
title	Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
title_short	Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
title_full	Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
title_fullStr	Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
title_full_unstemmed	Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
title_sort	recognition of pollution layer location in 11 kv polymer insulators used in smart power grid using dual-input vgg convolutional neural network
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/db1f6ec7a0df4bef991a0e487ca9daf5
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_version_	1718408350079647744

Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network

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