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Integration of Degradation Processes in a Strategic Offshore Wind Farm O&M Simulation Model

Author

Listed:
  • Thomas Michael Welte

    (SINTEF Energy Research, Department of Energy Systems, Trondheim 7465, Norway)

  • Iver Bakken Sperstad

    (SINTEF Energy Research, Department of Energy Systems, Trondheim 7465, Norway)

  • Espen Høegh Sørum

    (SINTEF Energy Research, Department of Energy Systems, Trondheim 7465, Norway)

  • Magne Lorentzen Kolstad

    (SINTEF Energy Research, Department of Energy Systems, Trondheim 7465, Norway)

Abstract

Decision support models for offshore wind farm operation and maintenance (O&M) are required to represent the failure behavior of wind turbine components. Detailed degradation modelling is already incorporated in models for specific components and applications. However, component degradation is only one of many effects that must be captured in high-level strategic decision support models that simulate entire wind farms. Thus, for practical applications, a trade-off is needed between detailed degradation modelling and the level of simplicity of input data representation. To this end, this paper discusses two alternative approaches for taking into account component degradation processes in strategic offshore wind farm O&M simulation models: (1) full integration of the degradation process in the O&M simulation model; and (2) loose integration where the degradation process is translated into simplified input to the O&M model. As a proof-of-concept, a Markov process for blade degradation has been considered. Simulations using the NOWIcob O&M model show that the difference between full and loose integration is small in terms of aggregated output parameters such as average wind turbine availability and O&M costs. Although loose integration models some effects less accurately than full integration, the former is more flexible and convenient, and the accuracy is for most purposes sufficient for such O&M models.

Suggested Citation

  • Thomas Michael Welte & Iver Bakken Sperstad & Espen Høegh Sørum & Magne Lorentzen Kolstad, 2017. "Integration of Degradation Processes in a Strategic Offshore Wind Farm O&M Simulation Model," Energies, MDPI, vol. 10(7), pages 1-18, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:925-:d:103519
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    References listed on IDEAS

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    1. Nicolai, Robin P. & Dekker, Rommert & van Noortwijk, Jan M., 2007. "A comparison of models for measurable deterioration: An application to coatings on steel structures," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1635-1650.
    2. Ossai, Chinedu I. & Boswell, Brian & Davies, Ian J., 2016. "A Markovian approach for modelling the effects of maintenance on downtime and failure risk of wind turbine components," Renewable Energy, Elsevier, vol. 96(PA), pages 775-783.
    3. Mahmood Shafiee & Maxim Finkelstein, 2015. "A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines," Journal of Risk and Reliability, , vol. 229(5), pages 373-384, October.
    4. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    5. Shafiee, Mahmood & Finkelstein, Maxim & Bérenguer, Christophe, 2015. "An opportunistic condition-based maintenance policy for offshore wind turbine blades subjected to degradation and environmental shocks," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 463-471.
    6. Shafiee, Mahmood, 2015. "Maintenance logistics organization for offshore wind energy: Current progress and future perspectives," Renewable Energy, Elsevier, vol. 77(C), pages 182-193.
    7. Christophe Bérenguer & Antoine Grall & C. Guedes Soares, 2011. "Advances in Safety, Reliability and Risk Management - ESREL 2011," Post-Print hal-02273237, HAL.
    8. Shafiee, Mahmood & Finkelstein, Maxim, 2015. "An optimal age-based group maintenance policy for multi-unit degrading systems," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 230-238.
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    Cited by:

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