Empirical end-device disturbance recognition by waveform feature learning models
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Paper number
119
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Moon, Sang-keun, Korea Electric Power Cooperation (KEPCO), Korea Republic of
Joung, Jong-man, Korea Electric Power Cooperation (KEPCO), Korea Republic of
Lee, Byungsung, KEPCO Research Institute, Korea Republic of
Kim, Jin-o, Hanyang University, Korea Republic of
Joung, Jong-man, Korea Electric Power Cooperation (KEPCO), Korea Republic of
Lee, Byungsung, KEPCO Research Institute, Korea Republic of
Kim, Jin-o, Hanyang University, Korea Republic of
Abstract
A waveform holds recognizable feature patterns. To extract such features of various equipment disturbance conditions from the waveform, we present a practical model to estimate distribution line (DL) conditions by means of a multi-label extreme learning machine. The motivation for the waveform learning is to develop device embedded models which are capable of detecting and classifying abnormal operations on the DLs. The model with the real DL waveform data holds a potential for determining additional DL conditions and improving its classification performance through the update mechanism of the learning machine. On the other hand, conditional structures for distribution networks are discovered with respect to distribution network configurations and measurement device characteristics by the time-scaled class map of obtained measurement data from the field.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/426
http://dx.doi.org/10.34890/651
http://dx.doi.org/10.34890/651
ISSN
2032-9644
ISBN
978-2-9602415-0-1