## 5 Summary and Conclusions  
This study has aimed to explore the information contained in high frequency SCADA data to determine characteristics and limitations of wind turbine SCADA data. The main goal of this contribution has been to quantify the information lost due to temporal aggregation of operating data, as this data is usually only available as 10-min averaged values. Simple methods such as the calculation of a set of descriptive statistics and the Kolmogorov-Smirnov test of the original and aggregated signal haven been carried out. Both methods, though, do not provide a clear picture of information loss. Although they show resolution-critical signals, they fail to provide any quantification of the effect of signal aggregation or indications that help to choose the optimal resolution for the signal. To address this limitations a framework for information loss study has been elaborated. The results of this method highlight wind data and electric signals as heavily affected by information loss with less than 50% of the data with error below 2.5% of the interquartile range of the data. Temperature signals are generally less sensitive to aggregation, with the noticeable exceptions of the temperatures of the gearbox that show similar losses to the electrical signals. The presented framework allows to rank and determine the expected information for each signal and a certain aggregation period. A study of seasonal behavior has revealed that for signals measured at the gearbox, i.e., gearbox oil and gearbox shaft bearing temperatures, the information loss only varies approximately 10% between summer and winter months. Information loss is a phenomenon that affects all turbines of the analyzed wind farm, but variations in the aggregation error are seen between turbines. Besides these approaches that pool together the whole operating regime, also the effect of wind speed on information loss has been investigated. Our study reveals that for various signals, temperatures in particular, ramping up from a stopping to rated power state causes the largest variations in the extent of information loss. Variations of an error in a 10 min aggregation interval of up to 400 kW for the active power, up to 400 A for the currents, and up to 3C for the main bearing temperature are the most noticeable examples of this investigation. In addition to these considerations, a methodology to choose the optimal signal resolution is provided. To comply with stricter conditions in terms of maximum acceptable error the period of aggregation of the signal should be reduced, requiring larger resources to handle and store the signal. In conclusion, this research delves into the limitations of typical 10-min SCADA operating data, investigates the effect of data aggregation and provides methods to determine the amount of information that is lost. Wind and electrical signals, and to a less extent temperatures of the gearbox are heavily affected by information loss and should, therefore, be stored at high resolutions of 1 to 5 s. The typical SCADA data resolution of 10 min is not sufficient to capture the dynamic behavior of these signals. The differences between the original and aggregated signal could negatively impact the performance of predictive algorithms and models describing normal turbine behaviors. Knowing the limitations of SCADA is also useful to explain the shortcomings of turbine models. Smarter SCADA data aggregation policies should be considered taking into account the issue of information losses on the various signals. Future works on this topic can attempt to quantify information loss in terms of local minima or maxima that are lost due to aggregation. This would give further insight into information loss on the signal dynamics. An approach might be the application of Fourier transform to the data to study changes in its dynamics. However, fast changing signals, in particular wind speed, might vary too randomly, eventually making it very difficult to isolate meaningful frequencies.