# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 3 Uncertainties
When determining a power curve, the wind speed is also corrected to reference values for turbulence and air density, for example. Further details on the methods and the associated uncertainties can be found in the IEC standard 61400-12-1/2 [30; 31]. Further uncertainties can arise when KPI definitions are changed, different definitions are used or from an inaccurate formulation of the definition. As KPIs are usually calculated and compared in a closed operational management software or benchmark platform, consistent definitions can be assumed for the entire portfolio and all periods. However, this uncertainty group should be considered whenever precalculated KPIs from different sources or systems are compared. The IEC standard 61400-26-1/2 [36; 37] already provides four exemplary definitions of the time-based availability and three definitions of the production-based availability, let alone many more company-specific versions. Data handling related uncertainties originate from the way how data are collected, processed, and stored. Some of the listed uncertainty causes can be easily avoided by simple measures or proper documentation of the data, and are thus not further investigated. Examples are missing information on the time zone of timestamps, a missing synchronization, or missing information on the time allocation (start, center, end) of an aggregation period. Uncertainties due to different data filtering approaches have been investigated by [PERSON] et al. [16] for power curves. This work focuses on the remaining three uncertainty causes. * **Data resolution :** Operational data of WT are mostly stored in resolutions of 5, 10, or 15 min. Older projects also use resolutions as low as 30 min, whereas recent projects sometimes store Hz data. Figure 1: Exemplary causes for uncertainties of performance KPIs grouped by their categories in a causal diagram. When data is aggregated, information like extreme values gets lost in the averaging process. This behavior is not to be confused with the Nyquist-Shannon sampling theorem, which determines the necessary sampling rate in a measurement [38,39]. The IEC 61400-12 [30,31] requires for example, a minimum sampling rate of 1 Hz for most measurements--ambient temperature or air pressure can also be sampled once per minute--whereas it defines aggregated 10 min data to be the foundation for power curve calculations. This work expects the underlying measurement to be done correctly. An additional uncertainty cause is related to the sample length of an aggregation period. Whenever samples are missing in the aggregation period, the remaining samples are weighted more strongly than in other periods. Although missing samples might show up in Hz data, they might not be detectable in 5 min data anymore. [PERSON] [40] investigated the information loss when switching from 1 min data to 15 min data in a different context. * **Data gaps/data availability:** Data gaps in a long history of operational data are normal. As shown in [41], the data availability is, on average, at 95%. In most cases, the data availability is higher (median at 99%), but in other cases also much lower. Current data availability requirements seem somewhat arbitrary. The German guideline for site assessment [29] requires, for example, a data availability of 80% without any further notices or restrictions. The technical guideline 10 of the FGW e.V. [42] allocates data gaps to be downtime in its availability calculation. Reasons or data gaps are manifold and include failed sensors, downtime of the whole turbine, including its controller, as well as telecommunication interruptions [43]. Many WT still use the GSM standard of the cellular network. If data are missing, the remaining data points are unlikely to be representative of the period considered. Thus, uncertainties in the calculated KPIs are to be expected. Existing literature mainly deals with different approaches to fill data gaps [38,44,45] and not with their effect on KPIs.