Active Yaw Control for Increased Wind Farm Production

The wakes (volumes of slower and turbulent wind) behind the turbines in a wind farm result in production losses for the downstream turbines by up to 20%. This effect can be mitigated using an active yaw control, by deflecting the wake propagation away from the other turbines, hence increasing the total production.

Video showing how the wake path behind a turbine can be modified by yawing the turbine. The software used is FAST.Farm

Background

In wind farms, turbines positioned downstream—within the wake of upstream turbines—experience reduced wind speeds. This leads to production losses of approximately 10-20%. Additionally, the turbulence within the wakes subjects these trailing turbines to higher fatigue loads due to the fluctuating wind conditions.

One solution to mitigate these effects is to adjust the yaw angle of the turbines, redirecting the wake away from downstream turbines (see the video). By deflecting the wake, the overall power output of the wind farm could potentially increase. However, this yaw adjustment can also influence the structural loads on the yawed turbines, possibly affecting their longevity.

To fully understand the trade-offs, it is crucial to investigate both the potential boost in power production and the impact on fatigue loads for the yawed turbines.

This project utilized the newly released FAST.Farm software to explore these dynamics, assessing the potential for increased power generation as well as the associated consequences on the structural health of the yawed turbines.

Contour plots showing the undeflected and deflected wakes at steady-state when the turbines have been yawed by the optimum yaw angle. Notice how the last row of turbines are not yawed.

Research outcomes

The project developed and implemented an innovative wind farm active yaw control (AYC) algorithm within the FAST.Farm software to evaluate its potential for increasing wind farm power production. At the same time, the impact of this control strategy on the fatigue loads of yawed turbines was thoroughly examined.

The project produced several key outcomes:

A game-theoretic active yaw control strategy, combined with an artificial neural network (ANN), was created for the wind farm. Results demonstrated that this approach can lead to a maximum 2.6% increase in power production.
Consistent with findings from other studies, this research confirms that active yaw control does not significantly increase overall turbine fatigue loads. However, while some components, such as tower-top torque and tower-base fore-aft moment, do experience higher fatigue loads, others, like the blade root out-of-plane moment and tower-base side-to-side moment, experience reduced loads.
Active yaw control is highly dependent on farm-level wind speed, direction, and turbulence intensity measurements, all of which greatly influence the results. Further research is needed to develop improved methods for accurately estimating these critical factors at the wind farm level.