## 4 Analyses
### Discussion  
The two analyses show that for the wind speed dependence of the information loss there is no shared pattern between all investigated signals. Nevertheless, we identified regions where maxima of the error due to aggregation can be located within the signals investigated: Below the operational regime, i.e., \(<\)5 m/s, in the transition state of the turbine from around 6 m/s to 12 m/s, and in the cut-off region above 25 m/s. In the first region, the turbine is typically either in idle state, turned off, just starting up, or right after shutting down. The error in the generator and rotor speed most likely is caused by this start-up/shut-down transition. The asymmetry of the error in Figure 11 supports this assumption as the error for lower values of \(<\)3 m/s is positive, i.e., the aggregation value is higher than the raw value. This behavior is the consequence of the rotational speed going down. In this particular case, also the pitch angle might be part of the transition to an idle state as the asymmetric behavior is the same with the opposite sign. The complete shutdown sequence might, however, contain important information about the health of the system that is lost by aggregation. The second region, corresponding to the part load region of the power curve is critical for various signals. As this is a transition phase from idle condition to the full power regime, the turbine behavior is highly dynamic, leading to short term variations in the operational data. Therefore, aggregated and original signals diverge considerably. The asymmetry of roughly 5 m/s in the error span and the mean of the active power, currents, and also voltages in Figure 11 shows a general overestimation of the raw data values around 7 m/s and an underestimation for higher values around 12 m/s. In contrast, for signals like main bearing, top box, and ambient temperature as well as the yaw angle there are no regions with large information losses. The range of variation is almost constant along the whole wind speed range. The span curves of Figure 11 might be misleading here: The error ranges are mainly below 1 \({}^{\circ}\)C. For a control parameter like the yaw angle this might be due to the fact that it is not directly connected to any operational state. Nevertheless, it is quite surprising that even some temperature signals of bearings do not exhibit a maximum in this dynamic transition region. One reason is most probably the thermal inertia of the material, especially for large bearings. There is a further group of signals with the voltages that have a higher information loss also for very high wind speeds, i.e., above 25 m/s. Here, the cut-out process of the turbines could be the main influence. In the same region also the yaw angle has an error maximum. Information about the cut-out process will, therefore, be lost, when aggregating the data in low resolutions. Again other signals, such as wind direction, rotor and generator speed, and the temperature inside the nacelle have their highest errors for low wind speeds, i.e., below 5 m/s. In this region the wind direction changes more often. The errors in the generator and rotor speed most likely result from the turbine turning up or down. However, this might contain important information about the health of the system. The wind speed itself carries increased error with increasing wind speed. Its simultaneous increase in underestimation by aggregation is most likely caused by sudden bursts of wind that barely contribute to an aggregated mean value. These observations provide useful insights on the behavior of turbine signals under specific wind conditions. In particular, they show that the accuracy of aggregated measurements is not independent from wind conditions. Gearbox behavior, for example, can vary visibly within the part load region of the power curve. Higher frequency of the data would be more appropriate to monitor and characterize these operating conditions.