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Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning

Author

Listed:
  • Jinseok Kim

    (Department of KDN Electric Power IT Research Institute, KEPCO KDN, Naju 58322, Korea)

  • Ki-Il Kim

    (Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea)

Abstract

Partial discharge (PD) detection studies aiming at the fault diagnosis for facilities and power cables in transmission networks have been conducted over the years. Recently, the deep learning models for PD detection have been used to diagnose the PD fault of facilities and cables. Most PD studies have been conducted in the field, such as gas-insulated switchgear (GIS) and power cables for high voltage transmission networks. There are few studies of PD fault detection for on-site low-voltage distribution networks. Additionally, there are few studies of PD detection algorithms for improving the accuracy of the deep learning models using small real PD data only. In this study, a PD online detection system and a model for long-term operational sustainability of on-site low voltage distribution networks are proposed using convolutional neural network (CNN) transfer-learning. The proposed PD online system makes it possible to acquire as many real PD data as possible through continuous monitoring of PD occurrence. The PD detection accuracy results showed that the proposed CNN transfer-learning models are more effective models for obtaining improved accuracy (97.4%) than benchmark models, such as CNN and support vector machine (SVM) using only small real PD data acquired from PD online detection system.

Suggested Citation

  • Jinseok Kim & Ki-Il Kim, 2021. "Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4692-:d:541471
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    References listed on IDEAS

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    1. Xiu Zhou & Xutao Wu & Pei Ding & Xiuguang Li & Ninghui He & Guozhi Zhang & Xiaoxing Zhang, 2019. "Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm," Energies, MDPI, vol. 13(1), pages 1-13, December.
    2. Ning Liu & Bo Fan & Xianyong Xiao & Xiaomei Yang, 2019. "Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
    3. Jiejie Dai & Yingbing Teng & Zhaoqi Zhang & Zhongmin Yu & Gehao Sheng & Xiuchen Jiang, 2019. "Partial Discharge Data Matching Method for GIS Case-Based Reasoning," Energies, MDPI, vol. 12(19), pages 1-15, September.
    4. Vo-Nguyen Tuyet-Doan & Tien-Tung Nguyen & Minh-Tuan Nguyen & Jong-Ho Lee & Yong-Hwa Kim, 2020. "Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 13(8), pages 1-16, April.
    5. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
    6. Yanxin Wang & Jing Yan & Zhou Yang & Tingliang Liu & Yiming Zhao & Junyi Li, 2019. "Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network," Energies, MDPI, vol. 12(24), pages 1-19, December.
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    Cited by:

    1. Xiaohua Zhang & Bo Pang & Yaxin Liu & Shaoyu Liu & Peng Xu & Yan Li & Yifan Liu & Leijie Qi & Qing Xie, 2021. "Review on Detection and Analysis of Partial Discharge along Power Cables," Energies, MDPI, vol. 14(22), pages 1-21, November.

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