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Partial Discharge Detection Based on Anomaly Pattern Detection

Author

Listed:
  • Jiil Kim

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

  • Cheong Hee Park

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

Abstract

Recently, a lot of research has been carried out on partial discharge (PD) using machine learning techniques. However, most of these studies have focused on the identification of multiple PD sources, PD classification, or denoising PD measurements, with few studies on real-time PD occurrence detection. In this paper, we propose a method to detect PD occurrence based on anomaly pattern detection. The proposed method consists of three steps. First, in the data preprocessing step, the pulse sequence data are converted into a feature vector stream by applying a sliding window technique. In the next step, normal data modeling is performed using feature vectors transformed from pulse sequence data collected in a normal state where no PD occurs. Finally, for the monitored pulse sequence, an online process for PD detection is carried out through conversion to a feature vector data stream and an anomaly pattern detection method. Experimental results using simulated PD data demonstrate the capabilities of the proposed method.

Suggested Citation

  • Jiil Kim & Cheong Hee Park, 2020. "Partial Discharge Detection Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(20), pages 1-11, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5444-:d:431029
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    References listed on IDEAS

    as
    1. Wong Jee Keen Raymond & Hazlee Azil Illias & Ab Halim Abu Bakar, 2017. "Classification of Partial Discharge Measured under Different Levels of Noise Contamination," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-20, January.
    2. Cheong Hee Park & Taegong Kim, 2020. "Energy Theft Detection in Advanced Metering Infrastructure Based on Anomaly Pattern Detection," Energies, MDPI, vol. 13(15), pages 1-10, July.
    3. 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.
    4. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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