Classification of Partial Discharge Measured under Different Levels of Noise Contamination

被引:29
作者
Raymond, Wong Jee Keen [1 ]
Illias, Hazlee Azil [2 ]
Abu Bakar, Ab Halim [3 ]
机构
[1] Tunku Abdul Rahman Univ Coll, Fac Engn & Built Environm, Dept Elect & Elect Engn, Kuala Lumpur, Malaysia
[2] Univ Malaya, Dept Elect Engn, Fac Engn, Kuala Lumpur, Malaysia
[3] Univ Malaya, UM Power Energy Dedicated Adv Ctr UMPEDAC, Wisma R&D UM, Level 4,Jalan Pantai Baharu, Kuala Lumpur, Malaysia
来源
PLOS ONE | 2017年 / 12卷 / 01期
关键词
FRACTAL FEATURE ENHANCEMENT; SUPPORT VECTOR MACHINE; POWER CABLE JOINTS; PATTERN-RECOGNITION; NEURAL-NETWORKS; WAVELET TRANSFORM; ACOUSTIC-EMISSION; EXTENSION METHOD; PD SOURCES; SIGNALS;
D O I
10.1371/journal.pone.0170111
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
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页数:20
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