Abstract
Advanced wind measuring systems like Light Detection and Ranging (LiDAR) is useful for wake management in wind farms. However, due to uncertainty in estimating the parameters involved, adaptive control of wake center is needed for a wind farm layout. LiDAR is used to track the wake center trajectory so as to perform wake control simulations, and the estimated effective wind speed is used to model wind farms in the form of transfer functions. A wake management strategy is proposed for multi-wind turbine system where the effect of upstream turbines is modeled in form of effective wind speed deficit on a downstream wind turbine. The uncertainties in the wake center model are handled by an adaptive PI controller which steers wake center to desired value. Yaw angle of upstream wind turbines is varied in order to redirect the wake and several performance parameters such as effective wind speed, velocity deficit and effective turbulence are evaluated for an effective assessment of the approach. The major contributions of this manuscript include transfer function based methodology where the wake center is estimated and controlled using LiDAR simulations at the downwind turbine and are validated for a 2-turbine and 5-turbine wind farm layouts.
Highlights
Wind power installations require precise control throughout their lifetime for optimal power generation with minimal risk of loss of security and reliability
The Light Detection and Ranging (LiDAR) based closed-loop adaptive control for desired yaw angle is studied for a two turbine wind farm layout
Closed-loop adaptive control technique is based on identifying the transfer function for wind turbine model which gives an actual yaw angle as the output and wake center estimation model which determines the nominal wake center position
Summary
Wind power installations require precise control throughout their lifetime for optimal power generation with minimal risk of loss of security and reliability. Wind turbines placed in a particular fashion in a wind farm often experience low power production and high turbulence owing to wake effects from upwind turbines. Wind wakes lead to low power production, reduce system efficiency and necessitate appropriate control action for the entire wind farm. Wake effects from adjacent wind turbines termed as wake mixing lead to change in ambient wind field conditions that alter dynamic loading on the downwind turbines [1]. Wake models with varying fidelity have been developed over the years with Jensen’s and Frandsen’s model being used widely for calculating velocity deficit for a given downwind turbine. Added turbulence to the ambient wind field is a significant cause for concern among wind farm operators
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