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Mirski, Marcin

University of British Columbia

This thesis presents a combined low-fidelity control-oriented dynamic parametric wake model and wind turbine platform motion model for floating offshore wind farms, named FLOw Redirection and Induction Dynamics for Floating turbines (FLORIDynFloat). This model builds on the existing FLOw Redirection and Induction Dynamics (FLORIDyn) model by adding effects arising from wind turbine translation on the sea surface and incorporating horizontal turbine platform motion simulation based on aerodynamic, hydrodynamic, and mooring line forces. These changes make the model suitable for use in floating wind farms where turbines are tethered to the sea floor. FLORIDynFloat models the relationship between turbine control inputs, incoming wind speed, and turbine positions and velocities; and the power generated by each turbine. The parametrised wake generated by a moving turbine is transformed between a local reference frame in which the turbine is stationary, and the global reference frame in which the wake's propagation and effects on other turbines can be calculated. The velocity of the moving turbine results in a correction to the amount of power generated, due to a changed relative wind speed. Based on current and previous values of turbine positions, velocities, and control inputs (turbine yaw and axial induction factor), the model outputs an estimate for the amount of power generated by each turbine, taking into account the aerodynamic coupling effects in a wind farm. In addition, the wake model is coupled to a previously published model for the dynamic motion of a floating wind turbine platform resulting from aerodynamic, hydrodynamic, and mooring line forces, to predict future turbine positions. Due to a lack of reference comparison, the model is validated by comparing its output to a modified static wake model in which the wake propagates at the relative wind speed in the local frame. The simulations performed, both with pre-defined and force-defined turbine motion trajectories, had realistic results. The model has sufficiently low computational complexity for use with a model-based controller such as model predictive control, and can serve to estimate the effects of control inputs on the overall power production of a floating wind farm.

Text

2021-07-06

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/78861

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/