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    Optimizing network replacement with AI

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    CIRED 2019 - 1739.pdf (281.0Kb)
    Paper number
    1739
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Faivre, Odilon, Enedis, France
    Cochet, Pierre, Enedis, France
    Mérigeault, Jérémie, Enedis, France
    Folleville, Sébastien, Enedis, France
    Abstract
    With 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.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/545
    http://dx.doi.org/10.34890/770
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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