Elsevier

Renewable Energy

Volume 156, August 2020, Pages 719-730
Renewable Energy

Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions

https://doi.org/10.1016/j.renene.2020.04.007Get rights and content
Under a Creative Commons license
open access

Highlights

  • Demonstrate model-based wind farm control in LES under time-varying inflow.

  • Ambient condition estimation with a novel theoretical measure of observability.

  • Provide a detailed tuning of FLORIS to LES data of the DTU 10MW wind turbine.

Abstract

Wind farm (WF) controllers adjust the control settings of individual turbines to enhance the total performance of a wind farm. Most WF controllers proposed in the literature assume a time-invariant inflow, whereas important quantities such as the wind direction and speed continuously change over time in reality. Furthermore, properties of the inflow are often assumed known, which is a fundamentally compromising assumption to make. This paper presents a novel, closed-loop WF controller that continuously estimates the inflow and maximizes the energy yield of the farm through yaw-based wake steering. The controller is tested in a high-fidelity simulation of a 6-turbine wind farm. The WF controller is stress-tested by subjecting it to strongly-time-varying inflow conditions over 5000 s of simulation. A time-averaged improvement in energy yield of 1.4% is achieved compared to a baseline, greedy controller. Moreover, the instantaneous energy gain is up to 11% for wake-loss-heavy situations. Note that this is the first closed-loop and model-based WF controller tested for time-varying inflow conditions (i.e., where the mean wind direction and wind speed change over time) at such fidelity. This solidifies the WF controller as the first realistic closed-loop control solution for yaw-based wake steering.

Keywords

Closed-loop wind farm control
Time-varying inflow
Wake steering
Ambient condition estimation
FLORIS
Large-eddy simulation

Cited by (0)

1

B. M. Doekemeijer is with the Delft Center for Systems and Control (DCSC), within the Data-Driven Control (DDC) research group of prof. J.W. van Wingerden.