# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 3 Uncertainties  
\"Uncertainty\" is a term and concept used in various ways. [PERSON] [21] describes it generically to be a situation where one does not know whether a statement or information is true or false. According to the Guide to the Expression of Uncertainty in Measurement (GUM) [22], it \"means doubt about the validity of the result of a measurement\". This definition, in the context of measurements and metrics that also includes KPIs, corresponds to the idea of this work. Uncertainties are closely related to sensitivities. Sensitivity analyses evaluate the effect of single parameters or a group of parameters on a result and are, for example, also used when assessing different scenarios. In the context of uncertainties, a sensitivity analysis helps to understand whether and how substantial deviations of one parameter affect the measurement or KPI and is performed as the first step of an uncertainty analysis. Sensitivity analyses can be distinguished into local and global analyses. A local sensitivity analysis evaluates the impact of deviations of an isolated parameter while keeping the other inputs stable, whereas a global sensitivity analysis varies all input parameters at the same time so that interdependencies can be discovered. Screening methods combine both attempts [22; 23]. Uncertainties are either aleatoric or epistemic. Aleatoric uncertainties are caused by the natural randomness of processes and are thus not predictable. Epistemic uncertainties are the result of a lack of information or knowledge and can be avoided or corrected in theory. [24] The standard deviation (\(\sigma\)) is a common metric to quantify uncertainties and assumes that the related results follow a normal distribution [22]. Density functions of single uncertainties can be combined to overall uncertainties through analytical or numerical methods. For this purpose, it is vital to know whether single uncertainties are independent or not [25; 26; 27; 28]. Utilization of uncertainties in the wind industry includes the determination of wind potential and energy yields [29], the determination of power curves [30; 31], or wind power forecasts [32; 33]. The results in corresponding expert reports, measurements, or predictions are displayed with uncertainty information to enable the consideration of individual safety requirements in decision-making processes. The only known publication related to uncertainties of KPIs in the wind industry is the already mentioned work of [PERSON] et al. [16] on the uncertainty of power curves. Figure 1 presents a fishbone diagram of possible causes of uncertainties collected for this work. The single causes haven been assigned to one of the four groups: data handling, measurement, method, and definition. As data handling-related uncertainties are the only uncertainties that are, to a certain  
extent, in control of the operational manager, this work focuses on this group. The other groups are briefly discussed below. Measurement uncertainties are the apparent causes of uncertainties for KPIs and well researched in general. Relevant references for uncertainties of inputs like power, wind speed, wind direction, and temperature are the International Electrotechnical Commission (IEC) standards 61400-12-1/2 [30; 31] (tables G1-G8) and 61400-15 [34], the technical guideline six [29] of the Fordergesellschaft Windenergie und andere Dezentrale Energien (FGW e.V. ), and the recommendations of the Measuring Network of Wind Energy Institutes (MEASNET) [35]. Default or standard values for individual uncertainties of the measurements are also given in the sources mentioned. Uncertainties resulting from corrections to the measured values are assigned to the uncertainty group \"method\". Among other factors, the wind speed measurement of the nacelle anemometer is corrected by a transfer function to account for the slowed down and disturbed wind speed at the measuring position behind the rotor.