A novel image‐orientation feature extraction method for partial discharges
Author:
Affiliation:
1. Department of Electrical Engineering Shanghai Jiao Tong University Shanghai China
2. Department of Light Sources and illuminating Engineering Fudan University Shanghai China
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Energy Engineering and Power Technology,Control and Systems Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/gtd2.12356
Reference21 articles.
1. Condition monitoring based on partial discharge diagnostics using machine learning methods: a comprehensive state‐of‐the‐art review;Lu S.;IEEE Trans. Dielect. Elect. Insul.,2020
2. Research on a practical de‐noising method and the characterization of partial discharge UHF signals;Wang Y.;IEEE Trans. Dielect. Elect. Insul.,2014
3. A new image‐oriented feature extraction method for partial discharges;Wang K.;IEEE Trans. Dielect. Elect. Insul.,2015
4. A convolutional neural network‐based deep learning methodology for recognition of partial discharge patterns from high‐voltage cables;Peng X.;IEEE Trans. Power Deliver.,2019
5. Random forest based optimal feature selection for partial discharge pattern recognition in HV cables;Peng X.;IEEE Trans. Power Deliver.,2019
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