NDLI: Neural network based PD source classification using a combined topology of unsupervised and supervised learning algorithm

Content Provider	IEEE Xplore Digital Library
Author	Chang, C. Su, Q.
Copyright Year	2001
Description	Author affiliation: Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Clayton, Vic., Australia (Chang, C.)
Abstract	Partial discharge distribution patterns derived from EEC270 defined parameters are investigated. It has been found that normative references can be employed not only to monitor the discharge activity level but also to identify different type of discharge sources. This is because the distribution pattern contains information about the origin of discharge source. PD distribution of phase resolved (PRPD) and pulse height resolved (PHPD) patterns are analyzed with the aid of a computer-based PD detector. The parameters of various distribution patterns are extracted to form a feature vector or fingerprint. There is an advantage of using the combined topology of unsupervised and supervised learning neural networks to train feature vectors of large size. The results in classifying PD sources on a few samples have confirmed the usefulness of this new approach.
Sponsorship	Power Eng. Soc.
Starting Page	1293
Ending Page	1298
File Size	697222
Page Count	6
File Format	PDF
ISBN	0780371739
DOI	10.1109/PESS.2001.970259
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Partial discharges Fault location Computerized monitoring Pattern analysis Distributed computing Phase detection Detectors Data mining Fingerprint recognition
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

Recognition of partial discharge patterns in motors using neural network

Feature Extraction for Multi Source Partial Discharge Pattern Recognition

Towards automated statistical partial discharge source classification using pattern recognition techniques

GNC-network: a new tool for partial discharge pattern classification

Application of neural network with genetic algorithm to UHF PD pattern recognition in transformers

Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources

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

Combining supervised and unsupervised monitoring for fault detection in distributed computing systems

Partial discharge pattern classification of angled point-oil-pressboard degradation

Neural network based PD source classification using a combined topology of unsupervised and supervised learning algorithm

Similar Documents

Recognition of partial discharge patterns in motors using neural network

Feature Extraction for Multi Source Partial Discharge Pattern Recognition

Towards automated statistical partial discharge source classification using pattern recognition techniques

GNC-network: a new tool for partial discharge pattern classification

Application of neural network with genetic algorithm to UHF PD pattern recognition in transformers

Pattern recognition techniques and their applications for automatic classification of artificial partial discharge sources

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

Combining supervised and unsupervised monitoring for fault detection in distributed computing systems

Partial discharge pattern classification of angled point-oil-pressboard degradation

Neural network based PD source classification using a combined topology of unsupervised and supervised learning algorithm