Recognition of pollution layer location in 11 kV polymer insulators used in smart power grid using dual-input VGG Convolutional Neural Network
| Autor: | L. Kalaivani, R. V. Maheswari, Vimal Shanmuganathan, B. Vigneshwaran, Seungmin Rho, Mi Young Lee, Seifedine Kadry |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
020209 energy Feature extraction 02 engineering and technology Convolutional neural network Signal 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering 0204 chemical engineering Convolution Neural Network Bayesian optimization Hyperparameter Feature fusion business.industry Deep learning Wavelet transform Pattern recognition Training optimizer High voltage insulator TK1-9971 Dual-input CNN General Energy Smart grid Electrical engineering. Electronics. Nuclear engineering Artificial intelligence business |
| Zdroj: | Energy Reports, Vol 7, Iss, Pp 7878-7889 (2021) |
| ISSN: | 2352-4847 |
| DOI: | 10.1016/j.egyr.2020.12.044 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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