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    A Genetic Algorithm Based Methodology for Prioritizing Maintenance Actions of Power Distribution Utilities

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    CIRED 2019 - 1246.pdf (298.3Kb)
    Paper number
    1246
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Pereira, Danilo, ENERQ - USP, Brazil
    Almeida, Carlos, ENERQ - USP, Brazil
    Kagan, Nelson, ENERQ - USP, Brazil
    Gouvêa, Marcos, ENERQ - USP, Brazil
    Junior, José, EDP, Brazil
    Junior, James, EDP, Brazil
    Viana, Fabrício, EDP, Brazil
    Dominice, Alexandre, EDP, Brazil
    Abstract
    Yearly, power distribution utilities plan preventive maintenance actions, aiming at reducing durations and frequencies of power outages. Currently, utilities planning engineers prioritize maintenance actions based on multiple spreadsheets, personal experience and cumbersome procedures. Typically, they simply rank the actions according to their expected benefits. Then a set of actions is selected depending on their costs and on the budgets available. This paper proposes a Genetic Algorithm (GA)-based methodology which provides an optimal plan of maintenance actions considering the financial restrictions. Four major types of actions are addressed, regarding the targeted assets: general Medium Voltage (MV) equipment, general Low Voltage (LV) equipment, MV/LV transformers and poles. To apply the proposed methodology, a case study was set up considering a Brazilian power distribution area. By executing the methodology for each of the four major types of actions, a plan of optimal maintenance actions was produced, considering the corresponding budget restrictions. The methodology succeeded in providing an optimal plan of suitable actions for a real power distribution substation.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/357
    http://dx.doi.org/10.34890/583
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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