Fingerprinting made easy by machine learningFingerprinting made easy by machine learning

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Paper number
93
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Jayaseelan, Sathiswar, TUE, Netherlands
Pondes, Albert, Enexis, Netherlands
Deursen van, Armand, TUE, Netherlands
Slootweg, Han, Eindhoven University of Technology / Enexis Netbeheer, Netherlands
Abstract
Circuit breakers are used to protect electrical networks and components from damages caused by high currents, resulting from overloads or short circuits. Malfunctioning of Medium Voltage (MV) circuit breakers often leads to a longer outage time and the outage spreads ­­to broader areas. Therefore, it is of great importance that the circuit breakers function properly. In order to test the proper working of MV circuit breakers, manual periodic measurements are carried out and the results are interpreted visually by the operator. This process requires much time, money and workforce. This paper discusses the possibility of automating the circuit breaker measurement system and subsequently the data analysis. An economical new tool was developed for automating the measurement system and a novel model was developed using machine learning for automating the data analysis process.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/282
http://dx.doi.org/10.34890/513
ISSN
2032-9644
ISBN
978-2-9602415-0-1