Probabilistic Forecasting of Wave Height for Offshore Wind Turbine Maintenance

James Taylor, Jooyoung Jeon

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
24 Downloads (Pure)

Abstract

Wind power continues to be the fastest growing source of renewable energy. This paper is concerned with the timing of offshore turbine maintenance for a turbine that is no longer functioning. Service vehicle access is limited by the weather, with wave height being the important factor in deciding whether access can be achieved safely. If the vehicle is mobilized, but the wave height then exceeds the safe limit, the journey is wasted. Conversely, if the vehicle is not mobilized, and the wave height then does not exceed the limit, the opportunity to repair the turbine has been wasted. Previous work has based the decision as to whether to mobilize a service vessel on point forecasts for wave height. In this paper, we incorporate probabilistic forecasting to enable rational decision making by the maintenance engineers, and to improve situational awareness regarding risk. We show that, in terms of minimizing expected cost, the decision as to whether to send the service vessel depends on the value of the probability of wave height falling below the safe limit. We produce forecasts of this probability using time series methods specifically designed for generating wave height density forecasts, including ARMA-GARCH models. We evaluate the methods in terms of statistical probability forecast accuracy, as well as monetary impact, and we examine the sensitivity of the results to different values of the costs.
Original languageEnglish
Pages (from-to)877-890
Number of pages14
JournalEuropean Journal of Operational Research
Volume267
Issue number3
Early online date18 Dec 2017
DOIs
Publication statusPublished - 16 Jun 2018

Keywords

  • OR in energy
  • Offshore wind operations and maintenance
  • Wave height
  • probabilistic forecasting

ASJC Scopus subject areas

  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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