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• W2812096095 abstract "This paper proposes a novel method for voltage dip classification using deep convolutional neural networks. The main contributions of this paper include: 1) to propose a new effective deep convolutional neural network architecture for automatically learning voltage dip features, rather than extracting hand-crafted features; 2) to employ the deep learning in an effective two-dimensional (2-D) transform domain, under space-phasor model (SPM), for efficient learning of dip features; 3) to characterize voltage dips by 2-D SPM-based deep learning, which leads to voltage dip features independent of the duration and sampling frequency of dip recordings; and 4) to develop robust automatically-extracted features that are insensitive to training and test datasets measured from different countries/regions. Experiments were conducted on datasets containing about 6000 measured voltage dips spread over seven classes measured from several different countries. Results have shown good performance of the proposed method: average classification rate is about 97% and false alarm rate is about 0.50%. The test results from the proposed method are compared with the results from two existing dip classification methods. The proposed method is shown to outperform these existing methods." @default.
• W2812096095 created "2018-07-19" @default.
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• W2812096095 date "2018-12-01" @default.
• W2812096095 modified "2023-10-02" @default.
• W2812096095 title "A Robust Transform-Domain Deep Convolutional Network for Voltage Dip Classification" @default.
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• W2812096095 doi "https://doi.org/10.1109/tpwrd.2018.2854677" @default.
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