Key performance indicators (KPIs) are commonly used in the wind industry to support decision-making and to prioritize the work throughout a wind turbine portfolio. Still, there is little knowledge of the uncertainties of KPIs. This article intends to shed some light on the uncertainty and reliability of KPIs in general and performance KPIs in particular. For this purpose, different uncertainty causes are discussed, and three data handling related uncertainty causes are analyzed in detail for five KPIs. A local sensitivity analysis is followed by a more detailed analysis of the related uncertainties. The work bases on different sets of operational data, which are manipulated in a large number of experiments to carry out an empirical uncertainty analysis. The results show that changes in the data resolution, data availability, as well as missing inputs, can cause considerable uncertainties. These uncertainties can be reduced or even mitigated by simple measures in many cases. This article provides a comprehensive list of statements and recommendations to estimate the relevance of data handling related KPI uncertainties in the day-to-day work as well as approaches to correct KPIs for systematic deviations and simple steps to avoid pitfalls.
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