# A Machine Learning Method for Modeling Wind Farm Fatigue Load  
[PERSON]  
[PERSON]  
[PERSON] [13] performed a study surrogate model for turbine loads in two-turbine farms. This surrogate model added different wind and control factors to the steady wind. The limitation of this paper is the accuracy of influence factors, which may differ in complex layouts. [PERSON] [14] applied the regression method to map the relationship between the load and de-rating control strategy. This method can estimate the short-term fatigue load. However, it does not consider the effect of yaw control. Finally, a multi-dimensional look-up table was proposed to predict the farm load in [15]. This method obtains the load prediction from interpolation, significantly reducing the computational time. However, with the increase in wind farms, this method will be time-consuming and inaccurate. Some studies have considered the balance between wind farm power generation and load [16; 17]. Nevertheless, these studies rely on computational fluid dynamics models, which have unaffordable computing time.  
In summary, the fatigue load prediction for wake steering control must have the following properties:  
* Running fast: The fatigue load predictions will be used in the optimization loop. Therefore, the computational time must be as short as possible.
* Nonlinear mapping capability: The ambient wind and wake interaction influence the fatigue loads. Thus, a complex nonlinear relationship exists between fatigue load and ambient wind and wake interaction.
* Considering wake interaction: The load of the downstream turbine is influenced by the upstream turbine's wake effects, including partial wake, wake meandering, and wake superposition.
* Considering wake deflection: The wake deflection affects the load as it changes the wake propagation direction by yaw misalignment, so wake deflection must be considered.  
Considering the above objectives, machine learning and artificial intelligence, such as SVM [18], GP [10], and DNN [19], are suitable for predicting the fatigue load because they can achieve the above goals at the same time.  
This paper proposes a machine learning model based on GP to predict fatigue loads under yaw misalignment. Firstly, a series of mid-fidelity simulations based on FAST.Farm [20] with yaw misalignment were designed to obtain the original load data, which considered the wake interaction between turbines. Secondly, the rainflow counting and Palmgren miner rules were introduced to transfer the original load to damage equivalent load. Finally, the GP model predicts the fatigue load after training by inputs and outputs. The main contributions of this paper are as follows: (1) A mapping from ambient wind, wake interactions, and yaw misalignment to fatigue damage at farm level; (2) the effects of yaw misalignment on blade root edgewise moment and blade root flapwise moment were measured.  
The remainder of this paper is organized as follows: Section 2 introduces the main framework and theoretical knowledge; the GP method and evaluation metrics are introduced in Section 3. Lastly, case study results and conclusions are presented in Sections 4 and 5, respectively.