USE CASE APPLYING MACHINE-LEARNING TECHNIQUES FOR IMPROVING OPERATION OF THE DISTRIBUTION NETWORK
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Paper number
2114
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Foros, Jørn, SINTEF Energy Research, Norway
Istad, Maren, SINTEF Energy Research, Norway
Morch, Andrei, SINTEF Energy Research, Norway
Mathisen, Bjørn Magnus, SINTEF Digital, Norway
Istad, Maren, SINTEF Energy Research, Norway
Morch, Andrei, SINTEF Energy Research, Norway
Mathisen, Bjørn Magnus, SINTEF Digital, Norway
Abstract
This paper discusses the use of machine learning (ML) techniques to improve fault handling in distribution networks. The paper includes a short survey on the use of ML techniques in fault handling and shows that little published work has been done on using weather data and smart metering data as data sources. It can be argued that this is desired to increase the performance and usability of ML in operational support systems. Previous work also focuses almost exclusively on statistical machine learning aiming to replace traditional simulation models, overlooking other ML methods which can support operations. Here it is illustrated that Case based reasoning (CBR) can be used to aid the decision-making for example, when trying to restore service after an outage. The paper also describes the use of experience databases to aid the operator during fault handling. To illustrate potential use of ML and CBR, the paper presents a use case for future fault handling in low voltage distribution network and discusses the usefulness of this approach. This example shows that implementation of ML techniques in daily operation can be expected to contribute to reduction of costs for the network companies and increased security of supply for the customers.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/735
http://dx.doi.org/10.34890/959
http://dx.doi.org/10.34890/959
ISSN
2032-9644
ISBN
978-2-9602415-0-1