Optimizing network replacement with AI

dc.contributor.affiliationEnedis
dc.contributor.affiliationEnedis
dc.contributor.affiliationEnedis
dc.contributor.affiliationEnedis
dc.contributor.authorFaivre, Odilon
dc.contributor.authorCochet, Pierre
dc.contributor.authorMérigeault, Jérémie
dc.contributor.authorFolleville, Sébastien
dc.contributor.countryFrance
dc.contributor.countryFrance
dc.contributor.countryFrance
dc.contributor.countryFrance
dc.contributor.detailedauthorFaivre, Odilon, Enedis, France
dc.contributor.detailedauthorCochet, Pierre, Enedis, France
dc.contributor.detailedauthorMérigeault, Jérémie, Enedis, France
dc.contributor.detailedauthorFolleville, Sébastien, Enedis, France
dc.date.accessioned2019-07-24T12:43:34Z
dc.date.available2019-07-24T12:43:34Z
dc.date.conferencedate3-6 June 2019
dc.date.issued2019-06-03
dc.description.abstractWith a network of 1.4 million km Enedis spends several hundred million Euros every year to maintain and renew the existing network by replacing parts of it.To be able to replace the network parts which are the most likely to be source of future faults is crucial for good capital expenditure (CapEx) management as it lowers operational expenditure caused by maintenance or incentive regulation rules.For the past three years Enedis has been using Artificial Intelligence (AI) to optimally choose which parts of the network have to be renewed.In this paper we detail the method, which was initially successfully developed for underground Low Voltage (LV) networks, and point out how it has been extended to underground Medium Voltage (MV) networks and overhead networks (LV and MV).(remark: this a non-defining relative clause, so you have to put a comma and can`t omit the relative pronoun)We also describe how big data technologies have allowed us to leverage on the work done to target network replacement in order to initiate a transition towards predictive maintenance.
dc.description.conferencelocationMadrid, Spain
dc.description.conferencenameCIRED 2019
dc.description.openaccessYes
dc.description.peerreviewedYes
dc.description.sessionNetwork components
dc.description.sessionidSession 1
dc.identifier.isbn978-2-9602415-0-1
dc.identifier.issn2032-9644
dc.identifier.urihttps://cired-repository.org/handle/20.500.12455/545
dc.identifier.urihttp://dx.doi.org/10.34890/770
dc.language.isoen
dc.publisherAIM
dc.relation.ispartProc. of the 25th International Conference on Electricity Distribution (CIRED 2019)
dc.relation.ispartofseriesCIRED Conference Proceedings
dc.titleOptimizing network replacement with AI
dc.title.number1739
dc.typeConference Proceedings
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