## 5 Conclusions  
The purpose of this project was to detect the severity of the economic loss due to the failure of the pitch mechanism in wind turbines and investigate the possible solutions. This work has identified that the real economic loss due to the downtime can be only captured if the market price is closely investigated. There were several events when the wholesale price was 5\(\times\) higher than the average monthly value. This number supports the case of reducing the amount of downtime to a minimum caused by hydraulic leak failure.  
The analysis was focused on three wind turbines of the wind farm. It was proven that the forecasted loss only for these three wind turbines could reach up to approximately $86,000, meaning that in 20-25 years which is the life expectancy of a wind farm, the total cost could end up as $340,000-430,000. Therefore, for a wind farm owner with more than 100 wind turbines in a wind farm the total cost could reach levels of $1,000,000 or more.  
Reinvesting this amount to the American wind industry using solutions such as up-tower repairs or retrofitting solutions could increase further savings. What is crucial is developing a relationship between the user and the component supplier. This amount could be invested there in order to establish a long-term viability of the specific repair industry. The most effective developments and product upgrades can be done if the information about the product and processes are directly communicated from the source. Working with refurbishment or component replacement there is a better possibility to establish a close relationship and accurately address possible malfunctions.  
Using the up-tower repair solution, the return of investment can be reduced for such turbines that have the same severity in failures, however it is unclear how much time could be saved. Further research could focus on acquiring data for longer periods (years) for multiple farms in different geographical regions, associating all the downtimes with the loss and based on machine learning forecasting accurately the expected damage and possibilities on savings.  
Conceptualization, G.K.; methodology, G.X.; validation, G.X. and G.K. investigation, G.K.; data curation, G.K.; writing--original draft preparation, G.K.; writing--review and editing, G.X.; visualization, G.X. and G.K.; supervision, G.X.  
This research received no external funding.  
This study was carried out in collaboration with Lind Jensen Machinery Inc., and therefore great thanks go to all the employees that have been involved in this study. The authors would also like to thank the utility and data provider company E.ON for their collaboration.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Turbine** & **Historic** & **2017** & **2018 Q1** & **Subtotal 15** & **Technician** & **Total 15** & **60 Months** \\  & **2017** & **Loss** & **Month** & **Costs** & **Months** & **Forecast** \\ \hline B13 & $2312 & $856 & $3168 & $3425.09 & $6593.33 & $32,966.64 \\ \hline C01 & $1751 & $452 & $2203 & $2744.65 & $4947.88 & $24,739.40 \\ \hline C13 & $1924 & $601 & $2525 & $3109.81 & $5635.27 & $28,176.37 \\ \hline
**Total** & & & **57897** & **59279.55** & **517,176.48** & **585,882.41** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Costs summary and 5-year forecast for the selected wind turbines.