# Considering Uncertainties of Key Performance Indicators in Wind Turbine Operation
## 1 Introduction  
After the successful installation of a wind farm (WF), the operational performance of its wind turbines (MTs) determines whether a project is economically successful or not. On average, operation and maintenance (O&M) accounts for approximately 25% to 35% [1; 2; 3; 4] of the levelized cost of energy (LCOE) and also affects the energetic and monetary revenue. Thus, the performance of WTs is of high importance and closely monitored by operational managers. As part of the digitalization of energy supply, the latest information technology (IT) applications and solutions also penetrate the area of O&M of WTs and WFs. Big data and artificial intelligence (AI) applications in particular support the operational managers with valuable information regarding the WT performance. As more and more tasks and evaluations are automated, reliable and trustworthy results become more important. KPIs are a common tool throughout all industries to make information easily accessible and comparable [5]. The wind industry is no exception and makes use of KPIs to evaluate the performance and reliability of WTs regularly. A whole set of KPIs is used to evaluate different aspects of WT operation, as shown in [6; 7]. Even though further tools like sophisticated machinelearning approaches for condition monitoring systems (CMS) [8] and performance monitoring applications [9] are used more and more often in the industry; KPIs remain the foundation for asset management, reporting, and benchmarking. As KPIs and their trends are part of many decision-making processes, it is necessary to take their reliability into account [5]. The reliability of a KPI can also be referred to as its uncertainty. Even though the uncertainties of KPIs have not been widely discussed in the literature, a few publications are available. [PERSON] et al. [10] evaluate the uncertainties of KPIs used for balanced scorecards caused by the selected sample size. [PERSON] et al. [11] discuss the uncertainty of environmental performance indicators on wastewater discharges, and [PERSON] et al. [12] deal with the uncertainty of energy consumption indicators for wastewater treatment plants due to measurement uncertainties and data availability limitations. Another example is the consideration of uncertainties by [PERSON] et al. [13] for a CO\({}_{2}\) emission indicator used in the cement industry. Publications on the sensitivity and uncertainty of KPIs related to the wind industry are rare. [PERSON] et al. [14] perform an analysis of the sensitivity of O&M costs on the chosen maintenance strategy; although this approach aims to identify cost reduction factors and not to identify and quantify uncertainties. The work of [PERSON] et al. [15] follows a similar approach and performs a sensitivity analysis of the wind plant performance to changes in WT design parameters. The only known publication on uncertainties of a performance KPI for WT operation was published by [PERSON] et al. [16], which examines the uncertainty in power curves due to different methods for data filtering and power curve modeling. This work is continued in another publication [17] to quantify the uncertainties of wind plant energy estimation resulting from power curve uncertainties and different sampling periods. A broader assessment of the uncertainties of O&M-KPIs used in the monitoring of WT is lacking. Thus, this paper discusses sensitivities and uncertainties of the most important performance KPIs and tries to quantify uncertainties to assess their relevance for the daily work. Another scope of this paper is to provide recommendations to reduce or mitigate KPI uncertainties by simple measures. The results are also meant to raise awareness of uncertainties when dealing with KPIs in general. The authors try to view uncertainties from the perspective of an operational manager and consider uncertainties that are relevant to and can be influenced by this specific role.