• DocumentCode
    740439
  • Title

    Economic evaluation of maintenance strategies for wind turbines: a stochastic analysis

  • Author

    Kerres, Bertrand ; Fischer, Katharina ; Madlener, Reinhard

  • Author_Institution
    Dept. of Machine Design, KTH R. Inst. of Technol., Stockholm, Sweden
  • Volume
    9
  • Issue
    7
  • fYear
    2015
  • Firstpage
    766
  • Lastpage
    774
  • Abstract
    The authors develop a stochastic model for assessing the life-cycle cost and availability of wind turbines resulting from different maintenance scenarios, with the objective to identify the most cost-effective maintenance strategy. Using field-data-based reliability models, the wind turbine - in terms of reliability - is modelled as a serial connection of the most critical components. Both direct cost for spare parts, labour and access to the turbine, as well as indirect cost from production losses are explicitly taken into account. The model is applied to the case of a Vestas V44-600 kW wind turbine. Results of a reliability-centred maintenance analysis of this wind turbine are used to select the most critical wind turbine components and to identify possible maintenance scenarios. This study reveals that corrective maintenance is the most cost-effective maintenance strategy for the gearbox and the generator of the V44 turbine, while the cost benefit of condition-based maintenance using online condition-monitoring systems increases with higher electricity price, turbine capacity and remoteness of sites.
  • Keywords
    condition monitoring; costing; life cycle costing; maintenance engineering; power system economics; power system reliability; wind turbines; Vestas V44 wind turbine; condition-based maintenance; cost effective maintenance strategy; critical wind turbine components; economic evaluation; electricity price; field data-based reliability model; gearbox; lifecycle cost assessment; online condition monitoring systems; power 600 kW; production losses; reliability centred maintenance analysis; serial connection; stochastic model; turbine capacity;
  • fLanguage
    English
  • Journal_Title
    Renewable Power Generation, IET
  • Publisher
    iet
  • ISSN
    1752-1416
  • Type

    jour

  • DOI
    10.1049/iet-rpg.2014.0260
  • Filename
    7209073