Wind turbine fatigue estimation is based on time-consuming Monte Carlo simulations for various wind conditions, followed by cycle-counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite-element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non-linear mechanics, and control. A Variational Auto-Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross-section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind-field measurement statistics, such as mean hub-height wind speed, standard deviation of hub-height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long-term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.

中文翻译：

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使用监督、控制和数据采集数据进行概率风力涡轮机叶片疲劳估计的条件变分自动编码器

风力涡轮机疲劳估计基于对各种风力条件进行耗时的蒙特卡罗模拟，然后是循环计数程序和工程损坏模型的应用。疲劳模拟的输出量大且维数高，因为它们通常包括对有限元计算网格的估计。应变和应力张量时间序列是考虑疲劳估计问题时主要关注的量，由于空气动力学、结构动力学、几何非线性力学和控制的耦合效应，由复杂的振动特性决定。训练变分自动编码器 (VAE) 以对模拟风力涡轮机叶片根部横截面上累积疲劳的概率分布进行建模。VAE 以与粗略风场测量统计相对应的历史数据为条件，例如平均轮毂高度风速、轮毂高度风速标准偏差和切变指数。在没有直接测量结构载荷的情况下，所提出的技术可用于根据历史监督、控制和数据采集 (SCADA) 数据进行长期概率恶化预测，同时捕获由于应变信息不完整而导致的固有任意不确定性当只有 SCADA 数据统计可用时，风力涡轮机结构的时间序列。