Results from a wake-steering experiment at a commercial wind plant: investigating the wind speed dependence of wake-steering performance

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.

FarmConners Wind Farm Flow Control Benchmark: Blind Test Results

“…Between P4 and P5, the difference in the comparison for both the upstream and downstream power predictions are expected to be driven by the prior calibration and the final selection of the parameters for the controlled periods. Specifically, the implemented yaw loss exponents n = 3 for P4 and n = 1.88 for P5 (see Table 6) are argued to be the main factor for the difference observed in the upstream power predictions; especially compared with the recent field calibration at the same site under WFFC, discussed in (Simley et al, 2021) where wind speed (or indirectly C T ) dependent values of n ≈ 2.2 − 2.3 for wind speeds between 4 -8 m/s, n ≈ 1.3−1.35 for 8 -12 m/s and n ≈ 0.36 for 12 -14 m/s are reported. On that regard, Figure 11 highlights the sensitivity of the widely adopted WFC-oriented models to the employed parameters and the importance of a comprehensive calibration data/process.…”

“…It consists of 7 Senvion MM82 wind turbines (diameter of 82 m, nominal power of 2050 kW, hub height of 80 m, see (Duc et al, 2019) for power and thrust coefficient, C T curves), organised in an irregular single row layout and labelled SMV1 to SMV7 from North to South. This wind farm has been used for the field tests of the French national project SMARTEOLE, whose results have been presented in Ahmad et al (2017) and Duc et al (2019) for the field campaign #1 and in Simley et al (2021) for the field campaign #3. The layout of the wind farm is shown on Figure 1 and the long-term wind rose observed at the site presented in Figure 2.…”

“…This ill-conditioned optimisation problem was solved by mapping the unknown parameters into an orthogonal space via the singular value decomposition (SVD), solving the identification by a maximum likelihood estimation in the reduced space, and then mapping back the solution to the physical space (Schreiber et al, 2020). The identified, tuned parameters for the wake model are shown in * In Simley et al (2021), the wake model fitting was realised by tuning the ambient turbulence intensity value rather than updating the model parameters. Within FLORIS (NREL, 2021), the Gauss-Velocity model Porté-Agel, 2014, 2016;Blondel and Cathelain, 2020;King et al, 2021), was used rather than the Gauss-Legacy Porté-Agel, 2014, 2016) as for P4 and P5.…”

“…Consequently the SMV7 active power signal must be corrected to be representative of SMV6 in baseline situation. This is done following the same procedure as explained in Simley et al (2021); namely under normal operation, SMV6 and SMV7 active power signals are binned against wind speed (1 m/s bins) and wind direction (calculated every degree on overlapping 10 • bins). Then, a transfer function is estimated by dividing the SMV6 averaged power by SMV7 averaged power in each bin.…”

“…Rockel et al, 2017;Bastankhah and Porté-Agel, 2019;Campagnolo et al, 2020;; and field experiments (e.g. Fleming et al, 2017;Annoni et al, 2018;Doekemeijer et al, 2021;Bossanyi and Ruisi, 2021;Simley et al, 2021). A comprehensive review of the power maximisation through WFFC is presented in Kheirabadi and Nagamune (2019) and Andersson et al (2021).…”