Wind turbine power curve upgrades: methods for the assessment and test cases study

Abstract

Abstract The research about wind turbine control and blade design optimization has flourished in the latest years and has provided the opportunity of diffusely updating the technology of operating wind turbines. Due to multivariate dependence of wind turbine power on ambient conditions and working parameters, it is complex to estimate the actual impact of power optimization strategies. This problem therefore calls for devoted operation data mining and statistical techniques, which are explored in the present work. In particular, two test cases of multi-MW wind turbines power upgrades are discussed: the former is a combined aerodynamic and control optimization, the latter is the optimization of the yaw control. The assessment of the upgrades impact is performed through the comparison between the post-upgrade measured production and a model estimate of the pre-upgrade production in the same conditions. The wind turbines nearby to the target upgraded ones are employed as references for the operation conditions and their working parameters are employed for a principal component regression of the power of the target wind turbine. The proposed method is general and, for the selected test cases, it arises that the aerodynamic and control optimization improves the Annual Energy Production of the order of the 3%, while the yaw control optimization provides a 1% AEP improvement.