NDLI: Empirical Modeling of Superconducting Fault Current Limiter Using Support Vector Regression

Content Provider	IEEE Xplore Digital Library
Author	In-Yong Seo Seong-Woo Yim Hye-Rim Kim Bok-Nam Ha Ok-Bae Hyun
Copyright Year	2002
Abstract	The superconductor-triggered type fault current limiter (STFCL), which was developed by Korea Electric Power Corporation (KEPCO) and LS Industrial Systems (LSIS), is under operation for the verification test at KEPCO's power testing center. The STFCL is composed of superconductor, fast switch and current limiting resistor. The fault current is suppressed after a half cycle by the method of a line commutation. In this paper, we investigated the empirical modeling of STFCL using principal components and auto-associative support vector regression (PCSVR) for the prediction and fault detection of the STFCL. Signals for the model are currents and voltages acquired from high-temperature superconductor (HTS), driving coil (DC) and current limiting resistor (CLR). After developing the empirical model we analyse the accuracy of the model. The results were compared with that of auto-associative neural networks (AANN). PCSVR showed much better performance in accuracy aspect. Moreover, this model can be used for the prognosis of STFCL system.
Sponsorship	Council on Superconductivity Appl. Superconductivity Conference Inc MIT
Starting Page	1254
Ending Page	1258
Page Count	5
File Size	672949
File Format	PDF
ISSN	10518223
Volume Number	20
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault current limiters High temperature superconductors Power system modeling System testing Switches Current limiters Resistors Large scale integration Fault currents Predictive models support vector regression Coated conductors high-temperature super- conductors modeling
Content Type	Text
Resource Type	Article
Subject	Condensed Matter Physics Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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