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Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN

Author

Listed:
  • Zhe Li

    (Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Yongpeng Xu

    (Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Xiuchen Jiang

    (Academy of Information Technology and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

Abstract

Pattern recognition of DC partial discharge (PD) receives plenty of attention and recent researches mainly focus on the static characteristics of PD signals. In order to improve the recognition accuracy of DC cable and extract information from PD waveforms, a modified deep belief network (DBN) supervised fine-tuned by the adaptive moment estimation (ADAM) algorithm is proposed to recognize the four typical insulation defects of DC cable according to the PD pulse waveforms. Moreover, the effect of the training sample set size on recognition accuracy is analyzed. Compared with naive Bayes (NB), K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural networks (BPNN), the ADAM-DBN method has higher accuracy on four different defect types due to the excellent ability in terms of the feature extraction of PD pulse waveforms. Moreover, the increase of training sample set size would lead to the increase of recognition accuracy within a certain range.

Suggested Citation

  • Zhe Li & Yongpeng Xu & Xiuchen Jiang, 2020. "Pattern Recognition of DC Partial Discharge on XLPE Cable Based on ADAM-DBN," Energies, MDPI, vol. 13(17), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4566-:d:408279
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    Citations

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    Cited by:

    1. Wenxia Pan & Xingchi Chen & Kun Zhao, 2022. "Cable-Partial-Discharge Recognition Based on a Data-Driven Approach with Optical-Fiber Vibration-Monitoring Signals," Energies, MDPI, vol. 15(15), pages 1-13, August.
    2. Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.

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