## 4 Analyses
### Discussion  
From the results of all turbines, we extracted the minimum \(p\)-value for each signal and aggregation value in Figure 3. Of course, this is a rather unconventional approach with questionable statistical significance. Nevertheless, in this way the results of the KS test can be divided into three signal subgroups that are shown in Table 3: (1) signals for which the test yields \(p\)-values of 1 or only slightly below. (2) Signals with a resulting \(p\)-value that decreases with increasing aggregation time, but never falls below the chosen level  
Figure 3: Calculated \(p\)-values of conducted KS tests for all signals at different levels of temporal aggregation for one exemplary turbine WT05 on the left and the minimum value of each cell of all turbines on the right. Cells with a red tilt are below the level of significance, the blue ones above.  
of significance of 0.05. (3) Signals that fall below the level of significance at least once for all turbines.  
For the first group of signals the \(p\)-value of the KS test was always much higher than our level of significance of 0.05, some even stayed permanently at 1. Although, from these results we can only conclude that the two samples, i.e., the raw data and the aggregated values, are not from two different underlying distributions, it is also a good sign, because it gives us an indication that most of the aggregated signals are not strongly disturbed with respect to the original signal for all resolutions. Other signals showed a decreasing \(p\)-value with increasing aggregation time. Still, most of the \(p\)-values were above our significance threshold. This could mean that, contrary to the first group, the signals deviate more and more with when decreasing the resolution. In the third group of signals, the KS test resulted in \(p\)-values below the level of significance at least once for all turbines. These low values of \(p<0.05\) occurred only for aggregation times of 600 s and only once or twice in the whole set of turbines. Therefore, they do not tell us that the respective signals are always altered heavily by the aggregation process. However, those data give us hints to signals that can show short term deviations in their time series that might be annihilated during mean value aggregation process. Therefore in return, these short term deviations only occur very rarely--in our case only for one or two turbines. Regarding the voltages, there was even only one prominent phase. The observed rarity also makes it hard to tell that the list of signals with eventual important short term deviations is complete. For example: All voltages should behave in the same manner.  
As a conclusion, the KS test revealed deviations for signals of which some fall into a group of typically fast changing values, such as the wind speed and direction, and the measured voltages. The other group consists of some rather lagged signals with several temperatures as well as the yaw angle. These signals might contain valuable information on short timescales that is lost during aggregation while it might serve as an important input for future predictive methods such as early fault detection. Finding these short-time features will certainly be a task for the learning process of such methods. Here, the KS test might be helpful to identify the interesting signals and time ranges. However, the KS test falls short of giving quantifiable indications on which resolution to choose.