Abstract
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.
Highlights
Wake steering is a wind farm control strategy for mitigating wake effects in which upstream wind turbines are misaligned with the wind, thereby deflecting their wakes away from downstream turbines (Dahlberg and Medici, 2003; Wagenaar et al, 2012; Boersma et al, 2017)
The FLOw Redirection and Induction in Steady State (FLORIS) model is tuned to match the depth of the measured baseline wake losses for SMV5 during the experiment by adjusting the turbulence intensity input, which affects the rates of wake recovery and expansion; we found that when using the “Gauss” velocity model, a turbulence intensity of 11 % represents the overall wake losses during the experiment reasonably well
For the 10–12 m/s wind speed bin, the energy gains at SMV5 are slightly greater than the improvements predicted using FLORIS with all three yaw offset calculation methods, yet very little change in energy is observed at SMV6 from yaw misalignment
Summary
Wake steering is a wind farm control strategy for mitigating wake effects in which upstream wind turbines are misaligned with the wind, thereby deflecting their wakes away from downstream turbines (Dahlberg and Medici, 2003; Wagenaar et al, 2012; Boersma et al, 2017). Doekemeijer et al (2021) described a wake-steering experiment at a wind plant in Italy in which two turbines were controlled to improve the net power production for either a row of three turbines or pairs of turbines spaced 5.2D to 6.5D apart, depending on the wind direction. Using both positive and negative yaw offsets, the authors observed increases in energy production of up to 35 % for the two-turbine scenario and 16 % for the row of three turbines while acknowledging net losses in energy production for certain wind directions.
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