## 2 Previous Work  
Studies in the wind energy sector using high-frequency SCADA data are still scarce as these data sets are rare. Industry practice and state of the art is using aggregated SCADA operating data as 10-min averages. Nonetheless, a few available publications show the potential of the utilization of high-frequency SCADA data [1; 2; 12; 13; 14; 15]. Generally SCADA data can be used for a high variety of applications. Two examples for major tasks in the wind energy sector are the prediction of the power output and the assessment of the turbine or component health status. Both problems are crucial for wind power production as they allow wind farm owners to keep their turbines spinning more and reduce mismatches between promised and delivered energy. Power production estimation is a very large field of study within wind energy. A wide array of methods are available to predict power output [16; 17; 18]. The approaches vary on the type of data that is fed to the algorithms as well as the strategy used to detect patterns in the data. SCADA data was used in Refs. [15; 19]. Since there is typically at least both the wind speed and the generated power within the SCADA data set, power output could ideally be estimated directly. However, this proved to be a very challenging task. Physical models focus mostly on the accurate prediction of the wind speed [20; 21]. By using the characteristic power curve of a turbine, it is then translated to a modelled power. As various models base on SCADA data and the resolution of this data is usually 10 min, i.e., rather low, it is relevant to determine the limitations of such aggregated data to better understand the possible shortcomings when predicting the power output. Figure 1: Illustration of temporal aggregation of SCADA operating data at various resolutions: On the left, a highly dynamic signal, the wind speed, loses much of its information even at low resolutions, while the original data of a slowly changing signal on the right, a temperature of the gearbox shaft bearing, is hidden behind the curve of the aggregation resolution of 10 s.  
[PERSON] et al. discussed the advantages of using high-frequency SCADA data in conjunction with a quantile random forest as predictive maintenance tool based on power curve modeling. The SCADA data used had a resolution of 4 s and was utilized to compare the predicted performance with 10-min averaged signals. As the natural variability of turbine operation was better captured by the high-frequency data, it also resulted in improved predictions [13]. In a later study, the same authors conducted a sensitivity study on the performance of high-frequency SCADA data as performance monitoring tool. Various factors such as terrain complexity, seasonality, choice of input variable, and most relevantly the sampling rate of data were analyzed. An important conclusion was the observation that a higher resolution allows to create more reliable models and as a key take-away they proposed to determine how much of the dynamic behavior of the signal is lost due to averaging [2]. Furthermore, the frequency of SCADA data is important to correctly model wake effects in wind farms. An inaccurate evaluation of wakes leads to imprecision in estimating wind speed and subsequent turbulence, which ultimately results in a poor prediction of power output [22]. A second important aspect in turbine operation is predictive maintenance. Unexpected and sudden failures can be very expensive for turbine owners and, therefore, an assessment of the turbine or component status shows up beneficial. The available data, the monitored system, and the requirements greatly influence the design and choice of a predictive maintenance toolbox. As previously mentioned the drivetrain and other mechanical components, such as bearings and shafts, can be monitored by measuring acceleration, displacement and vibration through specific sensors and via acoustics emissions by dedicated CMS [11].