NDLI: PD detection and recognition based on UHF method for typical models in air

Content Provider	IEEE Xplore Digital Library
Author	Da-Peng Duan Peng Wang Liang Han Yu-Hang Lu Wei Li
Copyright Year	2011
Description	Author affiliation: Beijing Electr. Power Res. Inst., Beijing, China (Da-Peng Duan; Peng Wang; Liang Han; Yu-Hang Lu; Wei Li)
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.
Starting Page	189
Ending Page	192
File Size	525174
Page Count	4
File Format	PDF
ISBN	9784886860743
e-ISBN	9784886860743
DOI	10.1109/ISEIM.2011.6826381
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-06
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	The Institute of Electrical Engineers, Japan
Subject Keyword	Partial discharges Atmospheric modeling Feature extraction Dielectrics Artificial neural networks Gas insulation Pattern Recognition Partial Discharge PD Models UHF measurement Wavelet Transforms Kernel Principal Component Analysis Artificial Neural Network
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Experimental study and feature extraction on UHF partial discharge detection for typical model in air

Feature Extraction for Multi Source Partial Discharge Pattern Recognition

Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

Partial discharge measurement and pattern recognition in gas insulated switchgear

Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

Partial discharge pattern recognition based on 2-D wavelet transform and neural network techniques

Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment

Building a Transformer Defects Database for UHF Partial Discharge Diagnostics

Partial discharge pattern recognition using multiscale feature extraction and support vector machine

PD detection and recognition based on UHF method for typical models in air

Similar Documents

Experimental study and feature extraction on UHF partial discharge detection for typical model in air

Feature Extraction for Multi Source Partial Discharge Pattern Recognition

Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

Partial discharge measurement and pattern recognition in gas insulated switchgear

Feature extraction method and neural network pattern recognition on time-resolved partial discharge signals

Partial discharge pattern recognition based on 2-D wavelet transform and neural network techniques

Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment

Building a Transformer Defects Database for UHF Partial Discharge Diagnostics

Partial discharge pattern recognition using multiscale feature extraction and support vector machine

PD detection and recognition based on UHF method for typical models in air