This paper proposes a hierarchical Bayesian model updating approach to quantify variability of aerodynamic stiffness and damping of an offshore wind turbine (OWT) under different environmental and operational conditions (EOCs) using in-situ vibration data and SCADA (supervisory control and data acquisition) over two months of continuous monitoring. The considered OWT is a Haliade 150, 6 MW GE turbine on a jacket substructure located at the Block Island Wind Farm in Rhode Island, USA. The OWT has been instrumented with a continuous monitoring system including an array of accelerometers and strain gauges. The modal parameters of the OWT are extracted using an automated system identification approach. These parameters exhibit significant variations under varying EOCs. This variation is more significant in natural frequencies and damping ratios of the first fore-aft bending mode due to the aerodynamic effects. In this paper, a modeling approach is proposed by introducing a spring and a damper at the nacelle level in the fore-aft direction to account for the observed aerodynamic effects. A hierarchical Bayesian model updating is formulated and implemented to update parameters representing the effects of aerodynamic stiffness and damping, as well as their statistical properties such as mean and covariance matrix which are updated as hyperparameters. To account for the correlation between aerodynamic effects and EOCs, the updating parameters are assumed to be functions of EOCs such as rpm and wind speed. Two levels of hierarchical Bayesian model updating are performed and compared. In level 1, only modal parameters are used in model updating, while in level 2, the assumed correlation between EOCs and modal parameters are accounted for to reduce the uncertainty of aerodynamic effects in the model. In addition to hyperparameters, the proposed hierarchical Bayesian approach provides statistical properties of error function by collecting the residual uncertainties that have not been accounted for, e.g., modeling errors, measurement noise and random disturbances. The predicted modal parameters using two levels of hierarchical Bayesian approach are compared with their identified counterparts, and the results indicate that level 2 approach significantly reduces estimation uncertainty in aerodynamic effects and produces model predictions consistent with observed values of natural frequencies and damping ratios.

中文翻译：

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在不同环境和运行条件下对海上风力涡轮机的空气动力学影响的分层贝叶斯量化


本文提出了一种分层贝叶斯模型更新方法，以量化海上风力涡轮机（OWT）在不同环境和运行条件（EOC）下空气动力学刚度和阻尼的变化，使用原位振动数据和SCADA（监督控制和数据采集）在两个月的持续监测中。考虑的 OWT 是位于美国罗德岛布洛克岛风电场导管架下部结构上的 Haliade 150、6 MW GE 涡轮机。OWT 配备了一个连续监测系统，包括一系列加速度计和应变计。OWT 的模态参数是使用自动系统识别方法提取的。这些参数在不同的 EOC 下表现出显着变化。由于空气动力学效应，这种变化在第一前后弯曲模式的固有频率和阻尼比中更为明显。在本文中，提出了一种建模方法，通过在前后方向的机舱水平引入弹簧和阻尼器来解释观察到的空气动力学效应。制定并实施了分层贝叶斯模型更新，以更新表示空气动力学刚度和阻尼影响的参数，以及它们的统计属性，例如更新为超参数的平均值和协方差矩阵。为了解释空气动力学效应和 EOC 之间的相关性，假设更新参数是 EOC 的函数，例如 rpm 和风速。执行并比较了两个级别的分层贝叶斯模型更新。 在级别 1 中，模型更新仅使用模态参数，而在级别 2 中，考虑了 EOC 和模态参数之间的假设相关性，以减少模型中空气动力学效应的不确定性。除了超参数之外，所提出的分层贝叶斯方法还通过收集尚未考虑的残余不确定性（例如建模误差、测量噪声和随机干扰）来提供误差函数的统计属性。将使用两级分层贝叶斯方法预测的模态参数与已确定的模态参数进行比较，结果表明，二级方法显著降低了空气动力学效应的估计不确定性，并产生了与观测值的固有频率和阻尼比一致的模型预测。