EAGER-DynamicData: Minimizing Wind Farm Operation and Maintenance Cost Using Data-Driven Models

在过去的几年里，风力发电场装置的增长导致了风力涡轮机数量的增加，这些涡轮机达到了它们的制造保修期。因此，风电行业目前面临着计划外维护成本的上升，这增加了运营和维护支出。该研究项目将开发新的操作和维护策略，以提高风电场系统的可靠性，从而降低风能成本。风力涡轮机在恶劣的条件下运行，导致风力涡轮机部件故障，这是难以预测的。因此，安排维护行动以避免或最小化此类组件故障是一项挑战。由于美国风力发电场的运营和管理成本不断上升，设计利用现有风力涡轮机传感器数据的方法对于降低风力发电场的运营成本至关重要。为了实现这一目标，本研究考虑了新的维护调度模型和算法，这些模型和算法考虑了涡轮机状态、天气条件和执行维护所需资源的可用性等数据的不确定性。如果成功，该探索性研究将使风能系统更快地进行初始维护响应，更好地利用有限的维护资源。有效利用昂贵的资源将促进竞争力，并将有助于降低风能成本，实现电价稳定，减少对全球燃料市场的依赖。有必要制定指导方针，以降低风能系统的运行和维护成本。为了实现这一目标，本研究的目的是建立随机在线数据支持模型和算法如何导致风力涡轮机快速损伤检测和故障减少。随机在线优化是解决这一问题的合适框架，因为它明确地将随时间演变的随机数据吸收到优化模型中，从而在观察整个随机数据流之前依次做出稳健的决策。该项目的动机来自于对风能维护计划的数据驱动方法的需求。提出的工作将解决基本的科学和工程挑战，以成功推导数据驱动的随机在线优化算法，用于风能系统的运行和维护。这项研究将通过提供计算和数据支持的概念、模型和算法，推进风力发电场运营和维护的最新技术。本研究的结果将通过引入：1)维护和资源调度的数据驱动优化模型和2)随机在线优化的新算法，将风能系统的维护计划扩展到一个新的水平，超越目前的实践状态。特别是，新的数据驱动方法，将随机规划与随机在线优化相结合，通常是孤立处理的，将为风电场等复杂多实体工程系统在不确定性下的随机数据同化决策提供一种新的可行方法。

The growth in wind farm installations in the past years has led to an increase in the number of wind turbines reaching the end of their manufacturing warranties. Therefore, the wind industry is now faced with a rising cost of unscheduled maintenance which is increasing operation and maintenance expenditures. This research project will develop new operation and maintenance strategies for improving the reliability of wind farm systems so that wind energy cost can be reduced. Wind turbines operate under harsh conditions that lead to wind turbine component failures, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions so that such component failures can be avoided or minimized. Because of the continuously escalating cost of wind farm operation and management in the United States, devising methods for using available wind turbine sensors data is critical to decreasing wind farms operational costs. To accomplish this objective, this research considers new maintenance scheduling models and algorithms that take into account data uncertainties in turbines status, weather conditions, and the availability of the resources needed to perform maintenance. If successful, this exploratory research will enable faster initial maintenance response and better utilization of limited maintenance resources in wind energy systems. The efficient utilization of costly resources will foster competitiveness and will contribute towards reducing the cost of wind energy, achieve electricity price stability, and reduce dependency on global fuel markets. There is a need to establish guidelines to reduce operation and maintenance costs in wind energy systems. In pursuit of this goal, the objective of this research is to establish how stochastic online data-enabled models and algorithms can lead to wind turbine rapid damage detection and failure reduction. Stochastic online optimization is a suitable framework for this problem since it explicitly assimilates stochastic data that evolve over time into the optimization model, enabling robust decisions to be made sequentially prior to observing the entire stochastic data stream. The project motivation comes from the need for a data-driven methodology for maintenance planning in wind energy. The proposed work will address basic scientific and engineering challenges toward the successful derivation of a data-driven stochastic online optimization algorithm for the operation and maintenance of wind energy systems. This research will advance the state-of-the-art in wind farm operation and maintenance by contributing computational and data-enabled concepts, models and algorithms. The outcomes of this research will extend how maintenance is scheduled in wind energy systems to a new level beyond the current state of practice by introducing: 1) data-driven optimization models for maintenance and resource scheduling and 2) new algorithms for stochastic online optimization. In particular, the new data-driven methodology, integrating stochastic programing with stochastic online optimization, which are usually treated in isolation, will give rise to a new and viable approach for stochastic data assimilation into decision-making under uncertainty for complex multi-entity engineered systems such as wind farms.