Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

Abstract

This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number (φ-q-n) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.

A Review of Online Partial Discharge Measurement of Large Generators

“…The number of neurons in input layer relies on the number of input features extracted, while that of output layer depends on the number of defined classes [109], i.e., the number of PD types to be classified. Compared with other classifiers, neural networks has the capability to learn knowledge from training samples and store in synapses and neurons during training process [110]. The back propagation algorithm is the most frequently used method for the training process.…”

Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

“…In this study, an electrical method that employs a UHF sensor is used for the PD measurement system. Time-resolved PD (TRPD) and phase-resolved PD (PRPD) analyses have been studied in order to examine the characteristics of PDs in GIS [18][19][20][21][22][23][24][25][26][27]. The TRPD-based method is a method of analyzing the time-domain features of PD pulses, frequency-domain features of PD pulses, and both time-domain and frequency-domain features of PD pulses [19][20][21].…”

“…The TRPD-based method is a method of analyzing the time-domain features of PD pulses, frequency-domain features of PD pulses, and both time-domain and frequency-domain features of PD pulses [19][20][21]. The PRPD-based diagnostic method analyzes phase-amplitude-number (φ-q-n) measurements, where φ is the phase angle, q is the amplitude, and n is the number of discharges [26]. It identifies the fault type by analyzing the number of PD pulses, the maximum amplitude, or the average amplitude in each phase [19][20][21]25,27,28].…”

“…It identifies the fault type by analyzing the number of PD pulses, the maximum amplitude, or the average amplitude in each phase [19][20][21]25,27,28]. From these features, fault types are classified by many methods, including a knowledge-based fuzzy logic analysis [26] and machine learning techniques such as K-means cluster analysis [23,29], artificial neural networks (ANNs) [19,27,30,31], or support vector machines (SVMs) [19][20][21]25,32,33]. Among the ANN and fuzzy logic methods, ANNs provide higher accuracy in classifying fault types and fault severities [26].…”

“…From these features, fault types are classified by many methods, including a knowledge-based fuzzy logic analysis [26] and machine learning techniques such as K-means cluster analysis [23,29], artificial neural networks (ANNs) [19,27,30,31], or support vector machines (SVMs) [19][20][21]25,32,33]. Among the ANN and fuzzy logic methods, ANNs provide higher accuracy in classifying fault types and fault severities [26]. However, existing methods have studied either feature extraction or classification for PD diagnosis.…”