## 4 Analyses
### Results
* It must be pointed out that the temperature of the gearbox oil and of the gearbox shaft bearings had a non-negligible error above 2 \({}^{\circ}\)C that slightly increased during the winter months. * This seasonal dependence was also present for resolutions of 300 s, 150 s and, partly, 60 s. Due to the very low overall error, it was no longer visible for higher resolutions. * The rest of the temperature signals were much less affected by seasonality and information loss in general. No clear differences between summer and winter months were seen in our analysis. Discussion  
Within the scope of our local error approximation, wind measurements and electrical signals are largely affected by a loss of information both at short and long aggregation periods. A certain cause for this behavior is the high variability of these signals. For temperatures, on the other hand, much less error is induced by aggregation. For example,  
Figure 8: Information loss results for all turbines partitioned by months. Two time resolutions are provided: 300 s in the top row, 600 s in the bottom row. Error bins are temperature intervals measured in degrees Celsius. the temperature of the main bearing has more than 90% of the data with an information loss between 0 and 0.025 IQR. Thermal inertia most likely play an important role, such that the data already undergo an intrinsic reduction of the dynamics. Within all investigated temperature signals, the gearbox and generator sensors are noticeable exceptions, as they show a substantial loss of information having less than 50% of the data within the smallest error bins for 10 min aggregation. Figure 5 not only helps to quantify information loss within different signals, but also determines which measurements are most critical and thus require shorter aggregation periods. The information loss phenomenon is relevant since it affects all turbines, as Figure 7 shows. Some turbines are affected more than others (WT04 and WT06 in the example), but overall all turbines show information loss. Moreover, knowing that some turbines are more affected than the others can be useful for modeling the behavior of the whole wind farm. In fact, these differences could indicate an existence of diverse behaviors within the turbines. However, these strong differences between the turbines within our data might also boil down to our randomly chosen sample size of 1000 h resulting in merely an average of 84 h per turbine. It is further observed that information loss referred to transmission related temperatures is affected by seasonality. Figure 8 shows signs of such effects. For the gearbox oil and gearbox shaft bearing temperatures, an increase of 10% in the data of the [0-1 \({}^{\circ}\)C] error bins of the winter months with respect to the summer could be related to the variability of environmental conditions. Lower and stable wind speeds lead to lower variations in operating conditions and, consequently, less dynamics in the form of steep gradients in the temperatures of the gearbox. This results in less differences between the original and aggregated signal. Similarly, differences in external temperatures might increase or decrease the gradients of component temperatures. Figure 9 shows the wind speed and ambient temperature along the year for an exemplary turbine WT05. Note that almost identical profiles are observed for the rest of the farm. As expected, summer months are warmer, but also the range of variation as well as the average value of wind speed is lower when compared to winter. This supports the assumption of an influence of the environmental variability on an increase in information loss. In conclusion, the choice of the aggregation period for each signal is subjected to a benefit-costs analysis in which the ultimate use of the signal as well as data storage and transmission costs play a contrasting role. However, to detect pre-failure states higher resolution might be needed as anomalies could manifest themselves on shorter timescales.