Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

Abstract

Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

“…With the advantages of small floor space and high reliability, gas-insulation switchgear (GIS) is outfitted in the power grid in large numbers and has already become a symbolic piece of equipment in power transmission and transformation system [1][2][3][4][5]. However, the internal latent defects caused by electricity, heat, and machinery action, as well as human factors in running GIS equipment, may result in equipment failure, or even induce large-area power failure accidents.…”

A Review of Online Partial Discharge Measurement of Large Generators

“…During last decade, various neural network models had been developed for PD classification, such as back propagation neural network, probabilistic neural network, radial basis function network and ensemble neural network etc. In [64,108,111], the authors reviewed these models exhaustively. Neural network is one of the most successful method used for PD pattern recognition.…”

An Ensemble-Boosting Algorithm for Classifying Partial Discharge Defects in Electrical Assets

“…Over the years, the SNN have been extensively being applied for recognizing PD patterns with great success [9,12,14]. ANNs are artificial intelligence models that imitate the way humans categorize patterns [24].…”

“…The back-propagation (BP) algorithm is the widely used algorithm for the ANN and has been very successful in recognizing PD patterns [12,14]. The BP algorithm is a kind of supervised learning, comprising the forward and backward learning [14,24]. In the BP algorithm, the input and output pattern data are continuously fed to the ANN and at each instant the error is backpropagated and the weights are updated until certain minimum error is accomplished.…”