NDLI: Electrical equipment fault diagnosis system based on the decomposition products of SF6

Content Provider	IEEE Xplore Digital Library
Author	Xin Ning Liyan Tian Xiaoguang Hu
Copyright Year	2009
Description	Author affiliation: School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing, 100191, China (Xin Ning; Liyan Tian; Xiaoguang Hu)
Abstract	This paper presents electrical equipment fault diagnosis system based on the decomposition products of SF6, and makes an introduction of a method that electrical equipment fault diagnosis system of hardware and software implementation. The hardware uses ATmega128 series single-chip platform, and the software uses advanced wavelet neural network fault diagnosis method. To prove the superiority of this algorithm, we make the simulation and comparison with others. A good synergy of hardware and software is used in SF6 electrical equipment fault diagnosis, and analysis of SF6 gas content of decomposition products to judge if the electrical equipment fault happens and to make a fault prediction. In this paper we will introduce the hardware design method and the detailed design of the software just because of strong electromagnetic interference environment and it is very important to the system design, finally by giving the experimental data to prove system reliability and practicality.
Starting Page	196
Ending Page	200
File Size	247280
Page Count	5
File Format	PDF
ISBN	9781424447060
DOI	10.1109/ICCA.2009.5410561
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-09
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Sulfur hexafluoride Electrical fault detection Hardware Object detection Software design Neural networks Electric variables measurement Equipment failure Magnetic field measurement neural network fault diagnosis electrical equipment wavelet transform
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

SF6 decomposition and fault type in SF6 gas insulated equipment

The neural network technology applied on fault detection of locomotive converter

Experimental study on the relationship between partial discharge and gas decomposition products in SF6 insulated electrical equipments

Study on SF6 decomposition characteristics under thermal fault and its representation method

Fault diagnosis based on discrete wavelet transform and ART2 neural network

Determination of the response of Ar + SF6 to crossed electric and magnetic fields using an artificial neural network

Fault Diagnosis Method Based on Wavelet Neural Network for Power System Turbo-Generator

Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor

Identification of fault types for a three-bus transmission network using Discrete Wavelet Transform and probabilistic neural networks

Electrical equipment fault diagnosis system based on the decomposition products of SF6

Similar Documents

SF6 decomposition and fault type in SF6 gas insulated equipment

The neural network technology applied on fault detection of locomotive converter

Experimental study on the relationship between partial discharge and gas decomposition products in SF6 insulated electrical equipments

Study on SF6 decomposition characteristics under thermal fault and its representation method

Fault diagnosis based on discrete wavelet transform and ART2 neural network

Determination of the response of Ar + SF6 to crossed electric and magnetic fields using an artificial neural network

Fault Diagnosis Method Based on Wavelet Neural Network for Power System Turbo-Generator

Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor

Identification of fault types for a three-bus transmission network using Discrete Wavelet Transform and probabilistic neural networks

Electrical equipment fault diagnosis system based on the decomposition products of SF6