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- W3152558267 abstract "Recent years, checking the status of electrical apparatus is gaining importance for assessing the quality and life of insulating material. Recognition and analysis of Partial Discharge (PD) activity both in single and multiple patterns are generally used as an indicator of insulation degradation over time. Furthermore, in real-world circumstances, PD can be generated either from single or multiple sources. However, multiple PD signals gets partially overlapped, which is very hard to identify them. The proposed work illustrates a new approach to predict partial discharge patterns using Deep Learning Algorithm (DLA). To demonstrate, three different simulated discharge conditions are investigated in this work: void, corona discharge, surface discharge and their combination. The deep learning algorithm with Convolution Neural Network (CNN) have the capability to predict the patterns using pre-trained networks. The Speeded Up Robust Feature (SURF) and Histogram of Oriented Gradients (HOG) feature extraction techniques compared with the proposed method." @default.
- W3152558267 created "2021-04-26" @default.
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- W3152558267 date "2021-03-25" @default.
- W3152558267 modified "2023-09-27" @default.
- W3152558267 title "Recognition of Single and Multiple Partial Discharge patterns using Deep Learning Algorithm" @default.
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- W3152558267 doi "https://doi.org/10.1109/icais50930.2021.9395881" @default.
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