Abstract
The optimization of wind energy conversion efficiency has been recently boosting the technology improvement and the scientific comprehension of wind turbines. In this context, the yawing behavior of wind turbines has become a key topic: the yaw control can actually be exploited for optimization at the level of single wind turbine and of wind farm (for example, through active control of wakes). On these grounds, this work is devoted to the study of the yaw control optimization on a 2 MW wind turbine. The upgrade is estimated by analysing the difference between the measured post-upgrade power and a data driven model of the power according to the pre-upgrade behavior. Particular attention has therefore been devoted to the formulation of a reliable model for the pre-upgrade power of the wind turbine of interest, as a function of the operation variables of all the nearby wind turbines in the wind farm: the high correlation between the possible covariates of the model indicates that Principal Component Regression (PCR) is an adequate choice. Using this method, the obtained result for the selected test case is that the yaw control optimization provides a 1% of annual energy production improvement. This result indicates that wind turbine control optimization can non-negligibly improve the efficiency of wind turbine technology.
Highlights
Wind turbines control optimization is a major topic in the scientific literature about wind energy.the possible applications are impressive and widely impact on the practical wind farm operation as well as on the future perspectives of horizontal-axis wind turbines technology.For example, it is remarkable that wind turbine control optimization can be conceived at the level of each single wind turbine or at the wind farm level
This work deals with the former type of approach; it is important to recall that cooperative control [1,2,3] and wake steering [4,5,6,7,8] are two closely related aspects, currently standing at the frontier in the wind energy research: the objective is adopting non-trivial yaw and-or pitch control strategies [9,10], in order to optimize the power production and possibly mitigate mechanical loads at the level of wind farm
The t-statistic (Equation (13)) is computed to be 0.4 and the hypothesis that the performance of T9 during Dbef and Daft come from the same statistical ensemble can not be rejected. These results indicate, consistently, that the performance of T1 has changed after the upgrade and this can be detected with the proposed models with statistical significance; on the other way round, can2019 be detected with statistical significance that a non-upgraded wind turbine has performance
Summary
Wind turbines control optimization is a major topic in the scientific literature about wind energy.the possible applications are impressive and widely impact on the practical wind farm operation as well as on the future perspectives of horizontal-axis wind turbines technology.For example, it is remarkable that wind turbine control optimization can be conceived at the level of each single wind turbine or at the wind farm level. This work deals with the former type of approach; it is important to recall that cooperative control [1,2,3] and wake steering [4,5,6,7,8] are two closely related aspects, currently standing at the frontier in the wind energy research: the objective is adopting non-trivial yaw and-or pitch control strategies [9,10], in order to optimize the power production and possibly mitigate mechanical loads at the level of wind farm. The practical application of this kind of control design technology evolution is the power capture efficiency improvement for wind turbines operating since a certain number of years. As regards this field of intervention, the attention is focused on the management of the blade pitches and of the yaw
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
