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

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.

Abnormal Data Detection and Identification Method of Distribution Internet of Things Monitoring Terminal Based on Spatiotemporal Correlation

“…Figure 11 shows the simulation results of the source identification of anomaly data under different LTU scales in the distribution Internet of Things using the algorithm in this paper and the FTAD algorithm. It can be seen that the recall and precision of the FTAD algorithm were greatly affected by the scale of the LTU in the distribution Internet of Things, increasing with the increase in the number of LTUs according to the principles of the FTAD [27]. A node was confirmed to be at the event boundary if the outcome was the dominant outcome in its neighborhood, which was largely affected by the size of the LTU.…”

Application of Evolutionary Artificial Intelligence. An Exploratory Literature Review

“…The action of detection is related to rare events, also called anomalies. Florkowski [25] proposed segmentation techniques for feature extraction, anomaly detection, and trend evolution using convolutional neural networks for the detection of coherent forms in the images. With the help of business analytics, companies, suppliers, and customers are connected into a single system where information and data are shared.…”