The Science of Making Torque from Wind (TORQUE 2018)

Authors:
Paul Fleming, Jennifer Annoni, Luis A. Martínez-Tossas, Steffen Raach, Kenny Gruchalla, Andrew Scholbrock, Matthew Churcheld, Jason Roadman

Abstract:
Wake steering is a form of wind farm control, in which intentional yaw misalignment of an upstream turbine, is used to deflect its wake away from a downstream turbine.  Recent research, primarily using large-eddy simulations (LES), has indicated that counter-rotating vortices are produced in wake steering, and these impact the behavior of wakes in important ways.  In this research, a lidar-based field campaign of wake steering is compared to saved LES results in order to validate the presence and behavior of these vortices on wakes generated using wake steering.

Authors:
Ervin Bossanyi

Abstract:
Turbine power and yaw set points can be adjusted across a wind farm to minimise the overall power losses and the additional fatigue loads caused by wake interactions. Detailed modelling is required to understand the complex flows in sufficient detail to allow a realistic practical control design. High-fidelity computational fluid dynamics requires enormous computational resources, so simpler engineering models are needed which capture the most important effects while running fast enough to allow sufficient testing. This paper describes a steady-state optimisation tool which has been extended to optimise all the power reduction set-points and yaw offsets simultaneously for different wind conditions. It also describes a fast time-domain simulation model which captures turbine and wake dynamic effects, so that wind farm controllers of all kinds can be tested in realistic and time-varying conditions. To demonstrate its application for controller testing, the performance of the combined power and yaw controller is tested during changing conditions of wind speed, direction and turbulence derived from measured site data. Finally, the need for validation is discussed, as many uncertainties still need to be resolved in order to obtain sufficient confidence that the potential benefits of such wind farm control schemes can be realised in practice.

Authors:
Johannes Schreiber, Bastian Salbert, Carlo Luigi Bottasso

Abstract:
In this work, a control-oriented wind farm model is validated against SCADA data from a typical on-shore wind farm, without additional instrumentation available. The comparison of model-predicted and measured power deficits due to wake impingement shows good agreement. Furthermore, the model is used to compute optimum yaw misalignments for yaw-induced wake steering, leading to an estimated 1.7% increase in annual energy production by mitigation of wake losses. Results show that wake steering based on standard SCADA data, which is usually available in operational wind farms, has promising potential for open-loop model-based wind farm control.

Authors:
Bart Matthijs Doekemeijer, Sjoerd Boersma, Lucy Y Pao, Jan-Willem van Wingerden

Abstract:
Wind farm control research typically relies on computationally inexpensive models for real-time optimization. However, due to changing atmospheric conditions and tough-to-model flow and turbine dynamics, these models need constant calibration. In this work, a novel real-time calibration solution for a dynamic wind farm model is presented, showing adaptability to changing freestream conditions and modeling errors at a low computational cost of 0.3 s per timestep on a single node with 12 cores. This work presents an essential building block for real-time wind farm control using computationally efficient dynamic wind farm models.

Authors:
Alberto Fortes-Plaza, Filippo Campagnolo, Carlo Luigi Bottasso, Jiangang Wang, Chengyu Wang

Abstract:
Wake steering by active yawing of upstream wind turbines is a promising wind plant control technique. To enable the development of model-based wind plant control methods, there is a need for models that can marry the contrasting requirements of good fidelity and low computational cost. This paper presents a reduced-order model (ROM) obtained by directly compressing high-fidelity computational fluid dynamics (CFD) simulation data using the proper orthogonal decomposition (POD) method. At first, simulations of wake-interacting wind turbines are obtained for time-varying yaw settings using the lifting-line large-eddy simulation (LES) code SOWFA. Next, a ROM is synthesized from the CFD transient simulations, obtaining a discrete-time state-space model that captures the dominant dynamics of the underlying high-fidelity model with only a reduced number of states. The ROM is optionally augmented with a Kalman filter, which feeds back turbine power measurements from the plant to the model, enhancing its accuracy. Results obtained in realistic turbulent conditions show a good agreement between high-fidelity CFD solutions and the proposed POD-based ROM in terms of wake behavior and power output of waked turbines. Additionally, the ROM presents acceptable results when compared to wind tunnel experiments, including the capability of the model to partially correct for an intentionally built-in model mismatch.

Authors:
Wim Munters, Johan Meyers

Abstract:
Turbine wake interactions in wind farms result in decreased power extraction in downstream rows. The current work investigates dynamic induction and yaw control of wind farms for increased total power extraction. Six different wind farm layouts are considered, and the relative benefits of induction control, yaw control, and combined induction–yaw control are compared. Results show that, through coordinated control, turbine density can be increased significantly with the same wind-farm efficiency as an uncoordinated farm.