Prognostics and Maintenance Optimization for Wind Energy Systems

• Author / Creator

  Fang Fang Ding

• Maintenance management in wind energy industry has great impact on overall wind power cost. Optimizing maintenance strategies can substantially reduces the cost and makes wind energy more competitive among the energy resources. Due to the extreme conditions of remote or offshore sites where the wind turbines are installed, corrective maintenance and time-based preventive maintenance are the most adopted strategies in the wind industry in recent years. However, there is need to further reduce wind power cost via maintenance strategy improvement to increase its competitiveness. Industry and research community have been focusing on various maintenance strategies to save the maintenance cost.
  This thesis is devoted to developing cost-effective maintenance strategies for wind farms, focusing on conventional time-based maintenance optimization, and prognostics and condition-based maintenance (CBM) optimization within the CBM strategy framework.
  Studies are performed on improving corrective maintenance and time-based preventive maintenance strategies, which are currently widely adopted in wind industry. Opportunistic maintenance methods are proposed, which take advantage of economic dependencies existing among the wind turbines, and corrective maintenance chances, to implement preventive maintenance simultaneously. Imperfect preventive maintenance actions are considered as well. The methods demonstrate the immediate benefits of saving the overall maintenance cost for a wind farm.
  In the more advanced CBM strategy, the health conditions of components are monitored and predicted, based on which maintenance actions are scheduled to prevent unexpected
  failures while reducing the maintenance costs. Prognostic techniques are essential in CBM. In particular, the wind direction and speed around wind turbines are changing over
  time, which leads to instantaneously time-varying load applied to the wind turbines rotors. With focus on gearbox failure due to the gear tooth crack, an integrated prognostics method is developed considering instantaneously varying load condition. The numerical examples demonstrate that the gearbox remaining useful life prediction considering time-varying load is more accurate compared to existing methods under constant-load assumption. In a subsequent extended study, uncertainty in gear tooth crack initiation time is further considered for wind turbine gearbox prognostics method development. The method provides more accurate gearbox remaining useful life prediction compared to the results without considering time-varying load condition.
  This thesis also proposes a CBM method considering different turbine types and lead times, as well as the production loss during the shutdown time. The capability to accurately estimate the average maintenance cost for a wind farm with diverse turbines is a key contribution of the proposed method. In addition, this thesis accounts for the inaccuracy in the simulation-based algorithms that most complex problems are solved with. A numerical method for CBM optimization of wind farms is developed to avoid the variations in CBM cost evaluation, which leads to a smooth cost function surface and
  benefits the optimization process.
  The research in this thesis provides innovative methods for maintenance management in the wind power industry. The developed methods will help to significantly reduce the overall maintenance cost within either conventional maintenance or CBM strategies that the wind farm owners may apply. It will improve the competitive advantage of the wind energy, and promote a clean and sustainable energy future for the society in Canada and worldwide.

• Subjects / Keywords

  • Renewable energy
  • Condition based maintenance
  • Opportunistic maintenance
  • Life cycle cost
  • Prognostics
  • Artificial neural network
  • Gearbox
  • Wind power system
  • Crack propagation
  • Bayesian inference
  • Maintenance management
  • Imperfect maintenance
  • Numerical method
  • Prediction
  • Crack initiation
  • Remaining useful life
  • Wind farm

• Graduation date

  Fall 2018

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R31N7Z366

• License

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