DocumentCode
2259546
Title
PD detection and recognition based on UHF method for typical models in air
Author
Da-Peng Duan ; Peng Wang ; Liang Han ; Yu-Hang Lu ; Wei Li
Author_Institution
Beijing Electr. Power Res. Inst., Beijing, China
fYear
2011
fDate
6-10 Sept. 2011
Firstpage
189
Lastpage
192
Abstract
The UHF method can not only be used in gas insulation substation-GIS and transformer, but also can be used to detect PD happening in air or other insulation dielectric. On account of the three kinds of PD happening in dielectric, out of dielectric and surface of dielectric respectively, four typical PD models were designed and manufactured with aluminium, namely, air gap, air void in epoxy, needle-flat and surface of epoxy, by which the PD experiments were carried out in air. The UHF PD signals were detected by the developed twin-spiral UHF coupler and amplifier with band of 100MHz-1500MHz. For every model, 50 UHF PD records were stored by digital storage oscilloscope with sampling rate 20GHz. After denoising with the improved wavelet adaptive threshold method, and envelope extraction, 19 features parameters were extracted in time domain and frequency domain to recognize the PD source type. Finally, a classifier was designed with 3-layers back propagation artificial neural networks, and the test results show that the extracted features and classifier are sufficient to recognize the PD source correctly with high robustness.
Keywords
aluminium; microwave measurement; neural nets; partial discharge measurement; partial discharges; substation insulation; transformer insulation; 3-layers back propagation artificial neural networks; Al; GIS; PD detection; PD recognition; UHF PD signals; UHF coupler; UHF method; amplifier; denoising; digital storage oscilloscope; envelope extraction; frequency 100 MHz to 1500 MHz; frequency 20 GHz; gas insulation substation; insulation dielectric; transformer; wavelet adaptive threshold method; Artificial neural networks; Atmospheric modeling; Dielectrics; Feature extraction; Gas insulation; Partial discharges; Artificial Neural Network; Kernel Principal Component Analysis; PD Models; Partial Discharge; Pattern Recognition; UHF measurement; Wavelet Transforms;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical Insulating Materials (ISEIM), Proceedings of 2011 International Conference on
Conference_Location
Kyoto
Print_ISBN
978-4-88686-074-3
Type
conf
DOI
10.1109/ISEIM.2011.6826381
Filename
6826381
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