Results from a Wake-Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake-Steering Performance

Eric Simley, Paul Fleming, Nicolas Girard, Lucas Alloin, Emma Godefroy, Thomas Duc

Research output: Contribution to journalArticlepeer-review

39 Scopus Citations

Abstract

Wake steering is a wind farm control strategy in which upstream wind turbines are misaligned with the wind to redirect their wakes away from downstream turbines, thereby increasing the net wind plant power production and reducing fatigue loads generated by wake turbulence. In this paper, we present results from a wake-steering experiment at a commercial wind plant involving two wind turbines spaced 3.7 rotor diameters apart. During the 3-month experiment period, we estimate that wake steering reduced wake losses by 5.6 % for the wind direction sector investigated. After applying a long-term correction based on the site wind rose, the reduction in wake losses increases to 9.3 %. As a function of wind speed, we find large energy improvements near cut-in wind speed, where wake steering can prevent the downstream wind turbine from shutting down. Yet for wind speeds between 6-8 m/s, we observe little change in performance with wake steering. However, wake steering was found to improve energy production significantly for below-rated wind speeds from 8-12 m/s. By measuring the relationship between yaw misalignment and power production using a nacelle lidar, we attribute much of the improvement in wake-steering performance at higher wind speeds to a significant reduction in the power loss of the upstream turbine as wind speed increases. Additionally, we find higher wind direction variability at lower wind speeds, which contributes to poor performance in the 6-8 m/s wind speed bin because of slow yaw controller dynamics. Further, we compare the measured performance of wake steering to predictions using the FLORIS (FLOw Redirection and Induction in Steady State) wind farm control tool coupled with a wind direction variability model. Although the achieved yaw offsets at the upstream wind turbine fall short of the intended yaw offsets, we find that they are predicted well by the wind direction variability model. When incorporating the expected yaw offsets, estimates of the energy improvement from wake steering using FLORIS closely match the experimental results.

Original languageAmerican English
Pages (from-to)1427-1453
Number of pages27
JournalWind Energy Science
Volume6
Issue number6
DOIs
StatePublished - 2021

Bibliographical note

See NREL/JA-5000-79912 for article as published in Wind Energy Science Discussions

NREL Publication Number

  • NREL/JA-5000-81654

Keywords

  • field experiment
  • nacelle lidar
  • wake steering
  • wind farm control

Fingerprint

Dive into the research topics of 'Results from a Wake-Steering Experiment at a Commercial Wind Plant: Investigating the Wind Speed Dependence of Wake-Steering Performance'. Together they form a unique fingerprint.

Cite this