## 2 Previous Work
Additionally, for electrical and electronic components it is possible to apply CMS by analyzing current signatures in search of anomalous patterns [23]. These approaches are all based on utilization of very rich data, characterized by high sampling rates in the order of kHz. Here, signal processing techniques such as Fast Fourier transform, Hilbert-Huang transform, or wavelets analysis can be adopted [24; 25; 26; 27]. Nonetheless, alternatives to the implementation of dedicated CMS in a wind turbine exist. SCADA data, while available at a much lower frequency and far poorer in terms of information, has also proven as a valid and cheap instrument for turbine monitoring. A short investigation on the usability of high-frequency SCADA data for predictive maintenance has been carried out by [PERSON] et al. [14]. In order to obtain the condition of a turbine, a quite plain approach is to model the power output and compare it to its measured value. In this manner, defects can be detected--ybut not localized--when significant discrepancies are observed [28; 29]. So-called normal behavior models are another popular mathematical approach. These are regression models designed to predict the value of a key variable, capable of capturing the status of the studied system. A set of input variables is fed to an algorithm and the difference between the predicted and measured value is tracked. Large deviations are marked as anomalies and can be inspected further [30]. This goal can, e.g., be pursued by physical models [31] or neural networks [32]. Alternative approaches based on anomaly detection and fusion of multiple indicators and alarm logs, addressing generator and main bearing failures, have been proposed in [33; 34]. When located in a wind farm, also adjacent turbines can serve as a reference value [31]. Further research also investigated simulating load by means of SCADA data from a single turbine or even the farm [35; 36], eventually serving as an input for residual useful lifetime (RUL) estimations. Using the data of a whole farm can also serve to reconstruct an optimized flow through the area of this farm [37]. Dealing with the loss of information when temporally aggregating data or reducing the sampling rate is not an issue that occurs exclusively in wind energy. Nowadays, in cars even more data is collected by their electronic control units. Processing these data faces a similar problem: [PERSON] et al. investigated the effects of reducing the sampling rate of the driving data with a particular focus on so-called micro-driving decisions such as spontaneous accelerations [38]. One result of this highly dynamic behavior was that the amount of information does not decrease linearly when reducing the temporal resolution, but that there are resolution ranges where no further information is lost whereas it falls down rapidly for other ranges. Having considered the state of the art regarding wind turbine monitoring and power prediction, this paper aims to advance the understanding of information loss and possible limitations of SCADA data. In particular, the effect of low temporal resolution is analyzed and a quantitative method to determine the amount of lost information is detailed. Using this framework it is possible to determine signals that are most affected by downsampling and identify the influence of seasonal behavior and differences between turbines. An analysis of the effect of wind speed on information loss is also discussed, determining which operating conditions are affected the most by information loss. Finally, a methodology to choose an optimal aggregation frequency for a given signal is presented allowing to minimize the data storage footprint, while retaining most of the relevant information. In comparison with other high-frequency investigations this research offers the advantage of a large dataset consisting of more than one year of 1 Hz operating data that supports our conclusions.