A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables

Peng, Xiaosheng and Yang, Fan and Wang, Ganjun and Wu, Yijiang and Lee, Li and Li, Zhaohui and Ahmed Bhatti, Ashfaque and Zhou, Chengke and Hepburn, Donald M. and Reid, Alistair J. and Judd, Martin and Siew, Wah Hoon (2019) A convolutional neural network based deep learning methodology for recognition of partial discharge patterns from high voltage cables. IEEE Transactions on Power Delivery, 34 (4). pp. 1460-1469. ISSN 0885-8977 (https://doi.org/10.1109/TPWRD.2019.2906086)

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Abstract

It is a great challenge to differentiate partial discharge (PD) induced by different types of insulation defects in high-voltage cables. Some types of PD signals have very similar characteristics and are specifically difficult to differentiate, even for the most experienced specialists. To overcome the challenge, a convolutional neural network (CNN)-based deep learning methodology for PD pattern recognition is presented in this paper. First, PD testing for five types of artificial defects in ethylene-propylene-rubber cables is carried out in high voltage laboratory to generate signals containing PD data. Second, 3500 sets of PD transient pulses are extracted, and then 33 kinds of PD features are established. The third stage applies a CNN to the data; typical CNN architecture and the key factors which affect the CNN-based pattern recognition accuracy are described. Factors discussed include the number of the network layers, convolutional kernel size, activation function, and pooling method. This paper presents a flowchart of the CNN-based PD pattern recognition method and an evaluation with 3500 sets of PD samples. Finally, the CNN-based pattern recognition results are shown and the proposed method is compared with two more traditional analysis methods, i.e., support vector machine (SVM) and back propagation neural network (BPNN). The results show that the proposed CNN method has higher pattern recognition accuracy than SVM and BPNN, and that the novel method is especially effective for PD type recognition in cases of signals of high similarity, which is applicable for industrial applications.

  • Item type:Article
    ID code:67324
    Dates:
    Date
    Event
    1 August 2019
    Published
    19 March 2019
    Published Online
    12 March 2019
    Accepted
    Notes:© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:15 Mar 2019 14:26
    Last modified:23 Apr 2024 00:16
    URI:https://strathprints.strath.ac.uk/id/eprint/67324
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