Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation.

由于风速和风向的季节性变化，风力涡轮机经历随机载荷。这些恶劣的运行条件会导致风力涡轮机出现故障，而这些故障很难预测。因此，安排将避免故障的维护操作具有挑战性。在本文中，导出了一种用于风电场运行规划的仿真驱动在线维护调度算法。在线调度是解决此问题的合适框架，因为它将随时间演变的数据集成到维护调度决策中。本文中介绍的计算研究将模拟驱动的在线调度算法的性能与实践中常用的两种基准算法进行了比较：计划维护和基于状态的监控维护。使用现有的离散事件系统规范仿真模型来测试和研究所提出算法的优点。计算研究证明了在为风电场做出维护决策时避免过于简单化假设的重要性。例如，大多数文献假设维护提前期是恒定的。计算结果表明，允许以在线方式调整提前期可以提高风电场运营在涡轮机故障数量、可用性和发电量方面的性能。大多数文献都假设维护提前期是恒定的。计算结果表明，允许以在线方式调整提前期可以提高风电场运营在涡轮机故障数量、可用性和发电量方面的性能。大多数文献都假设维护提前期是恒定的。计算结果表明，允许以在线方式调整提前期可以提高风电场运营在涡轮机故障数量、可用性和发电量方面的性能。