Abstract
This paper introduces a framework to assess a hybrid data-driven wake steering strategy combining the advantages of an analytical model for the initial data gathering together with a data-driven model for modelling error correction. The main building blocks of the control strategy (required input domain, data gathering and model generation) are evaluated within the wind tunnel, utilising a newly developed setup that can simulate dynamic wind direction variations and which was specifically designed to examine wake steering control strategies. The control strategy employs a machine-learning algorithm to generate a turbine and wake model based on the measurement data, allowing this approach to be extrapolated for an arbitrarily large wind farm. The machine-learning model is used to estimate the optimal yaw angle in a closed-loop format. To implement the developed control strategy, steady measurements are analysed first to determine the required input variables. This is followed by an investigation of the potential power gain and the impact of utilising a grid-search procedure. The procedure minimises adverse conditions as it collects the required data points, still achieving a power gain.
Published Version
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.
