Partial Discharge Data Matching Method for GIS Case-Based Reasoning

Abstract

With the accumulation of partial discharge (PD) detection data from substation, case-based reasoning (CBR), which computes the match degree between detected PD data and historical case data provides new ideas for the interpretation and evaluation of partial discharge data. Aiming at the problem of partial discharge data matching, this paper proposes a data matching method based on a variational autoencoder (VAE). A VAE network model for partial discharge data is constructed to extract the deep eigenvalues. Cosine distance is then used to calculate the match degree between different partial discharge data. To verify the advantages of the proposed method, a partial discharge dataset was established through a partial discharge experiment and live detections on substation site. The proposed method was compared with other feature extraction methods and matching methods including statistical features, deep belief networks (DBN), deep convolutional neural networks (CNN), Euclidean distances, and correlation coefficients. The experimental results show that the cosine distance match degree based on the VAE feature vector can effectively detect similar partial discharge data compared with other data matching methods.

Deep Learning in High Voltage Engineering: A Literature Review

“…In 2019, the authors of [13] simulated PDs using a laboratory setup and collected PD data from a live substation. The four PD sources considered in this work that take place in a GIS platform are: floating electrode defects, metallic protrusion defects, insulation void discharge defects, and free metal particle discharge defects.…”

Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning

“…It achieved the highest accuracy of 97.41%. The authors [3] proposed variational autoencoder (VAE) using a data matching method. Cosine distance was used to classify PD data.…”

“…First, most PD detection studies have been conducted in the field, such as gas-insulated switchgear (GIS) and power cables for high voltage transmission networks [2,3]. There are few studies of PD fault detection for low voltage distribution networks closest to the electricity customers.…”

Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

“…In turn, supervised learning implies an algorithm's ability to recognize elements based on provided samples with the goal of recognizing new data based on training data. Supervised learning algorithms include, for example, decision trees, support vector machines (SVM), naive Bayes classifiers, k-nearest neighbors and linear regressions [7][8][9][10][11][12][13][14][15][16][17][18][19][20][34][35][36][37]. Supervised learning can be further divided into classification and regression: classification means that samples belong to two or more classes, with the goal of predicting the class of unlabeled data from the already-labeled data and thus identifying to which category an object belongs; regression is understood as predicting an attribute associated with an object.…”

“…In early applications, due to computational complexity, a strong reduction of the PD phase resolution was applied [8,9]. PD pattern recognition has been performed in various domains; i.e., it has been applied either to phase or pulse magnitude distributions [13], to a pulse time waveform [16,25,34] or to PD images [14,21,36]. The real challenge for this approach concerns patterns containing a superposition of multiple defects that occur in high-voltage electrical insulation [18,21,25].…”