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Cristin-resultat-ID:
1726219
Sist endret:
20. januar 2020, 15:12
NVI-rapporteringsår:
2019
Resultat
Vitenskapelig artikkel
2019
Use case applying machine-learning techniques for improving operation of the distribution network
Jørn Foros
Maren Kristine Istad
Andrei Z Morch
og
Bjørn Magnus Mathisen
Tidsskrift
Tidsskrift
CIRED Conference Proceedings
ISSN 2032-9644
NVI-nivå 1
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Om resultatet
Om resultatet
Vitenskapelig artikkel
Publiseringsår: 2019
Publisert online: 2019
Artikkelnummer: 2114
Open Access
Lenker
Lenker
Institusjonsarkiv
hdl.handle.net/11250/2618632
Beskrivelse
Beskrivelse
Engelsk
Tittel
Use case applying machine-learning techniques for improving operation of the distribution network
Sammendrag
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.
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Bidragsytere
Bidragsytere
Jørn Foros
Forfatter
ved Energisystemer ved SINTEF Energi AS
Maren Kristine Istad
Forfatter
ved Energisystemer ved SINTEF Energi AS
Andrei Zabourdiaev Morch
Bidragsyterens navn vises på dette resultatet som Andrei Z Morch
Forfatter
ved Energisystemer ved SINTEF Energi AS
Bjørn Magnus Mathisen
Forfatter
ved Software Engineering, Safety and Security ved SINTEF AS
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