# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 5 Results
### Data Availability  
All considered KPIs are affected by decreasing data availabilities since the data is less representative of the considered period. Figure 4 shows the coefficient of variation in the results for (**a**) the wind speed, (**b**) the wind power density, and (**c**) the capacity factor, depending on the data availability and compared to a reference value based on a 100% data availability. The coefficient of variation was chosen over the standard deviation to make different datasets comparable. The results illustrate that data availability is not the decisive factor, but rather the representativeness of the remaining data. Although the data availability is equivalent, short and random data gaps show only a minuscule effect, but long and subsequent data gaps lead to high uncertainties. Hereby, the results also emphasize the need of an entire year of measurements if representative results are to be achieved. The two depicted data gap distributions represent the best- and worst-case scenarios. For mixed experiments of shorter and longer data gaps, the coefficient of variation is expected to be between both cases. A metric for the quality or representativeness of the remaining time series is needed to estimate case-specific uncertainties for data gap distributions witnessed in the field. Different attempts of the authors as well as existing metrics, such as the work of [PERSON] [53], did not lead to satisfying results or were not applicable for the described case. Thus, there is need for further research. Figure 3: Effect of different data resolutions on (**a**) (wind power density) the calculation of an average wind power density and (**b**) (availability) availability metrics in the case that downtimes are solely identified based on SCADA-Data. As for the data resolution (Section 5.1), KPIs with a nonlinear relation to the wind speed measurement show a higher sensitivity to decreasing data availability. When comparing the results for the wind speed (Figure 4a) to the wind power density (Figure 4b) and the capacity factor (Figure 4c), the coefficient of variation is more than twice as high. Even though the power curve of a WT represents the relation between two measurements (wind speed and power) and should not be affected by data gaps, the experiments carried out in this work showed increasing uncertainties with decreasing data availabilities, see Figure 5. Still, the uncertainties are much lower compared to the examples in Figure 4. If the data gaps are randomly distributed, the uncertainty of the resulting annual energy production is even for a data availability of just 20% below 0.2% and thus negligible. Subsequent data gaps cause not only considerably higher aleatoric uncertainties but also systematic deviations. These uncertainties can be mainly related to seasonal effects, such as varying air densities and turbulence intensities. By correcting the power curves for both effects [31; 54], the uncertainties can be significantly reduced. At a data availability of 80%, the uncertainty for subsequent data gaps can be reduced from approximately 0.6% to 0.3%. However, some systematic uncertainties remain, and long data gaps should be avoided whenever possible. Even though the uncertainties caused by random data gaps could be only slightly decreased, it is highly recommended to perform a density and turbulence correction of power curves in any case. Figure 4 also highlights the need for a comprehensive data set for power curve assessments. Less than three months of data (here 25% data availability) cause an increasing uncertainty as the wind speed range is not sufficiently covered in many cases. Again, the order of magnitude of the above values is considered valid, while the concrete uncertainties can vary for other datasets. If an empirical power curve is used to calculate the production-based availability, its uncertainty propagates into the yield loss calculation. Figure 4: Coefficients of variation for different KPIs caused by a reduction in data availability.