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

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