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Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns

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  • Marek Florkowski

    (Department of Electrical and Power Engineering, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland)

Abstract

In the resilient and reliable electrical power system, the condition of high voltage insulation plays a crucial role. In the field of high voltage insulation integrity, the partial discharge (PD) inception and development trends are essential for assessment criteria in diagnostics systems. The observed trend to employ more and more sophisticated algorithms with machine learning features and artificial intelligence (AI) elements is observed everywhere. The classification and identification of features in PD images is perceived as a critical requirement for an effective high voltage insulation diagnosis. In this context, techniques allowing for anomaly detection, trends observation, and feature extraction in partial discharge patterns are important. In this paper, the application of few algorithms belonging to image processing, machine learning and optical flow is presented. The feature extraction refers to image segmentation and detection of coherent forms in the images. The anomaly detection algorithms can trigger early detection of the trend changes or the appearance of a new discharge form, and hence are suitable for PD monitoring applications. Anomaly detection can also handle transients and disturbances that appear in the PD image as an indication of an abnormal state. The future monitoring systems should be equipped with trend evolution algorithms. In this context, two examples of insulation aging and application of PD-based monitoring are shown. The first one refers to deep convolutional neural networks used for classification of deterioration stages in high voltage insulation. The latter one demonstrates application of optical flow approach for motion detection in partial discharge images. The motivation for the research was the strive to machine-controlled pattern analysis, leading towards intelligent PD-based diagnostics.

Suggested Citation

  • Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3886-:d:583844
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    References listed on IDEAS

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    1. Irfan Ullah & Rehan Ullah Khan & Fan Yang & Lunchakorn Wuttisittikulkij, 2020. "Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment," Energies, MDPI, vol. 13(2), pages 1-17, January.
    2. Marek Florkowski & Dariusz Krześniak & Maciej Kuniewski & Paweł Zydroń, 2020. "Partial Discharge Imaging Correlated with Phase-Resolved Patterns in Non-Uniform Electric Fields with Various Dielectric Barrier Materials," Energies, MDPI, vol. 13(11), pages 1-15, May.
    3. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
    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. Simeng Song & Yong Qian & Hui Wang & Yiming Zang & Gehao Sheng & Xiuchen Jiang, 2020. "Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence," Energies, MDPI, vol. 13(16), pages 1-16, August.
    6. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
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    Cited by:

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    2. Nan Shao & Yu Chen, 2022. "Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation," Energies, MDPI, vol. 15(6), pages 1-19, March.

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