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

Marek Florkowski
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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

Energies, 2021, vol. 14, issue 13, 1-18

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.

Keywords: partial discharges; phase-resolved patterns; high voltage insulation systems; diagnostics; machine learning; deep learning; convolutional neural network; optical flow; image processing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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