Document Type
Article
Publication Date
1-1-2017
Abstract
Cable joint insulation breakdown may cause a huge loss to power companies. Therefore, it is vital to diagnose the insulation quality to detect early signs of insulation failure. It is well known that there is a correlation between Partial discharge (PD) and the insulation quality. Although many works have been done on PD pattern recognition, it is usually performed in a noise free environment. Also, works on PD pattern recognition in actual cable joint are less likely to be found in literature. Therefore, in this work, classifications of actual cable joint defect types from partial discharge data contaminated by noise were performed. Five crosslinked polyethylene (XLPE) cable joints with artificially created defects were prepared based on the defects commonly encountered on site. Three different types of input feature were extracted from the PD pattern under artificially created noisy environment. These include statistical features, fractal features and principal component analysis (PCA) features. These input features were used to train the classifiers to classify each PD defect types. Classifications were performed using three different artificial intelligence classifiers, which include Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It was found that the classification accuracy decreases with higher noise level but PCA features used in SVM and ANN showed the strongest tolerance against noise contamination.
Keywords
Accuracy, Adaptive neuro fuzzy inference system, Article, Artificial neural network, Cable joint insulation, Classification, Electric capacitance, Electrical parameters, Fractal analysis, Fuzzy system, Multivariate analysis, Noise, Noise contamination, Partial discharge, Principal component analysis, Protective equipment, Statistical analysis, Support vector machine
Divisions
fac_eng
Funders
University of Malaya and Malaysia Ministry of Higher Education (MOHE): MOHE HIR (H-16001-D00048), FRGS (FP043-2013B) and PPP (PG028-2016A)
Publication Title
PLoS ONE
Volume
12
Issue
1
Publisher
Public Library of Science