Empirical end-device disturbance recognition by waveform feature learning models

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CIRED 2019 - 119.pdf (599.84 KB)

Paper number

119

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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

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.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/426
http://dx.doi.org/10.34890/651

ISSN

2032-9644

ISBN

978-2-9602415-0-1