Offshore wind farm opportunity maintenance strategy based on failure risk level
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    Abstract:

    The maintenance cost of offshore wind farms accounts for a high proportion of the total cost of power generation. In order to reduce the operational and maintenance costs of offshore wind farms, based on a reliability threshold, a maintenance strategy is proposed in the paper that considers the fault-relatedness among subsystems and the subsystem fault risk levels of wind turbine units. This strategy includes fault risk levels, dual reliability thresholds, and a maintenance matrix. Firstly, a fault chain model is used to describe the fault relationships among subsystems of wind turbine units, and reliability models for each subsystem are established. Secondly, the concept of multi-level maintenance is introduced, and the optimization model of wind farm opportunity maintenance strategy considering the fault risk level is proposed. The risk factor of subsystem opportunity maintenance is optimized using particle swarm algorithm, and then the maintenance strategy of a single unit is determined. Thirdly, an offshore wind farm maintenance strategy optimization model based on the maintenance matrix is proposed by considering factors such as offshore accessibility and spare parts inventory. The model takes the minimum total maintenance cost per unit time as the objective function and dynamically adjusts the maintenance strategy according to the subsystem's spare parts inventory and failure risk level. Finally, a single wind turbine in an offshore wind farm is used as an example to analyse the impact of factors such as accessibility and multilevel maintenance on the wind farm opportunity maintenance strategy. The results show that the opportunity maintenance strategy considering the failure risk level can reduce the cost by 27.34% compared with the traditional opportunity maintenance strategy, which verifies the effectiveness of the proposed opportunity maintenance strategy in reducing maintenance costs.

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History
  • Received:May 15,2023
  • Revised:August 12,2023
  • Adopted:May 12,2023
  • Online: November 23,2023
  • Published: November 28,2023
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