Can engineering wake models be trusted? Practical model validation

Jens Madsen, Bjarke Dam, and Koji Fukami

Suzlon Blade Science Center, Vejle, Denmark

2nd Wind Energy Science Conference, Cork (IRE), Jun-2019 Active Wake Control Concept

Standard ”Greedy” control Wake Steering (yaw control) Baseline mode Redirection of wakes • Each turbine autonomously optimizes • Collective control via Power Plant Controller own settings • Sacrifice power+loads for front turbines. • Wake impacts are disregarded • Typical park-level AEP gains of 0.5 to 2% • Default operational mode for wind farms • Fatigue life consequences in ±2% range

2 Multi-fidelity Wake Model Hierarchy Different tools for different jobs ....

FLORIS: • Open-source, control-oriented framework. Minor model improvements compared with public version • Used to ... – Identify optimum control settings – design control algorithms

CFD/AD: • ABL flow model (RANS) with Actuator Discs and digital terrain import • Used to ... – validate and tune simpler models (FLORIS) – confirm steady-state AEP estimates – evaluate importance of terrain complexity

Turbine characteristics (power and thrust): • Tabulated from aeroelastic simulations, f(WS, yaw)

Cp Ct 3 FLORIS validation cases

1. Sexbierum 2. Operational wind farm(s) 3. CFD-based validation 4. Wake Control Experiment

S-97 prototype turbine in Texas

Cookhouse wind farm, South Africa S-97 prototype, Texas

• Single- or Double turbine wakes • Complex wakes, wind farm level • Simple cases, single wakes • Downstream LIDAR scanning of • Measured wind speed and T.I. In • Direction-binned production • Parametric studies to isolate steered wake, single WTG (S-97) wake(s) using mobile met masts data aggregated for wind farm effects and tune sub-models • Allow calibration of yaw-induced wake deflection models. Can we predict shape and decay Can we predict variation in wake Can we predict shape and decay of wake deficits? loss (power) over 360 degrees? of wake deficits? ”Wake Steerability” impacts potential AEP improvements !!

Normal operation Wake steering mode (zero yaw) (yaw offsets) 4 Normalized power of downstream turbines 1. Sexbierum – Double-wake case

Double-wake experiment: Wake downsstream of two aligned turbines (T38, T37) *

Wind Speed (m/s) • Classic campaign (1992/93) that measured turbine power and T38 wake profiles using multiple met masts at 6x3 turbine wind farm

• Measurements for wind speed range of 5-10m/s. Range with T37 roughly constant turbine characteristics (Ct=0.75, Cp=0.39) T36 • FLORIS wake decay model (Bastankhah) with default empirical constants. Turbulence level tuned from CFD. • Both CFD/AD and FLORIS match measurements well.

* Cleijne JW (1992): “Results of the Sexbierum wind farm; double wake measurements”, Technical Report 92-388, TNO, Environmental and Energy Research, Apeldoorn, Netherlands. 5 2. Operational Wind Farm Evaluating impact of terrain complexity

• Example: 20 unit onshore wind farm (S-88 turbines) • Use normalized (360-deg) power polars to assess model performance • Mildly complex terrain. Is it a problem that FLORIS ignores this? – Adjust normalization of CFD predictions based on local speed up effects – Example direction: CFD predicts 7% variation in front-row power. – Turbines inside wind farm: Larger variation due to diverse wake situations.

Front-row turbines

900000 800000 700000 0 600000 1 2 3 4 500000 5 6 400000 7 8 9Height of ground 300000 10 11 12 200000 13 100000 14 15 0 17 16 19 18

6 2. Operational Wind Farm Evaluating impact of terrain complexity

Farm efficiency • Reference for normalization • Orange line: Power for 8m/s wind speed at hub height

• Grey line: Maximum power for each wind direction. Accounts for local speed-up.

• Blue line: Model with flat terrain. Incoming wind speed identical for all directions.

• Primary terrain impact relates to local speed-up effects. • Adjusting for the difference in reference wind speeds, wakes are not significantly affected. • FLORIS should be able to handle Wind direction this site. 7 2. Operational Wind Farm (SCADA)

SCADA, shifted +5deg Low bin popuilations  Large uncertainty CFD 8m/s, ”Gaumond weighted”

• Considered nearly two years of filtered SCADA data (10-min avg). Lack of data in some direction bins (<120 degrees) • Simulation data modified by ”weighted directional binning” (Gaumond, 2014). Ensures comparability with binned observations. • Excellent agreement in polars for farm efficiency between CFD (@8 m/s) and filtered SCADA.

M Gaumond et al. (2014): “Evaluation of the wind direction uncertainty and its impact on wake modeling at the Horns Rev offshore wind farm”, Wind Energ. 2014; 17:1169–1178 8 2. Operational Wind Farm – Tuning FLORIS wake decay

• Good agreement with aggregated 360-degree power polar using FLORIS

• However, this masks turbine-level bias: FLORIS models tend to overestimate turbine power deep inside wind farms. We have studied operational wind farm with >160 units.

• Choice of wake decay constant (ka) should not necessarily be made based on the overall farm efficiency (aggregate power level)

9 3. CFD-based Validation Yaw-induced lateral wake redirection @ 20-deg yaw offset CFD results (u^3) vs. Jimenez model • Lack of good data sets for validation of yaw- induced wake deflection models

• FLORIS sub-model tuning based on CFD/AD simulations with parametric variations – Yaw offsets: from -20deg to +20deg – Ambient TI: from 6% to 18% – Multiple operational points (W.S, TSR, RPM), linear range

• Evident that TI influences magnitude of wake redirection.

• This is essential to wake control potnetial

CFD, TI = 15% • However, not reflected in existing sub-models CFD, TI = 12% CFD, TI = 9% CFD, TI = 6% 3. CFD-based validation Augmentation of the wake direction model of Jimenez

• Jimenez wake redirection model* has one free parameter (k)

∆푥 4 휉 ∙ 휉2 + 15 ∙ 1 + 2푘 훿푦 퐷 휉 ∙ 휉2 + 15 = − 퐷 ∆푥 5 30푘 30푘 ∙ 1 + 2푘 퐷 1 휉 = ∙ 퐶 ∙ cos2 푦푎푤 ∙ 푠푖푛 푦푎푤 2 푇

• FLORIS implementation has default value k=0.17 • We propose the following version that depends on ambient TI:

푘 = 12 ∗ 푇퐼2 + 0.08

* A.Jimenez, A,Crespo, and E.Migoya (2010): “Application of a LES technique to characterize the wake deflection of a wind turbine in yaw”, ”Wind Energy, Vol.13, No.6, p.559-572. 11 3. CFD-based Validation Augmentation of the wake direction model of Jimenez

FLORIS – 2017 : Coleman near-wake deflection model x2 FLORIS – 2019 : Coleman near-wake deflection model x1

12 4. Wake Control Experiment (Texas)

IEC met mast Galion LIDAR

S

1 S 1 1 9 7

13 4. Wake Control Experiment • Uptower installation (inside nacelle) of scanning LIDAR device. Dec-18 to Mar-19 • Internal hoist (500-kg capacity) used for lifting of device (85kg) • Customized mounting frame to overcome view obstruction challenges. • Measure the downstream wake behavior (1km range) as a function of yaw offsets. Horizontal scanning plane with 50-degrees angle. • Results used to validate and refine models used for wake control.

14 4. Wake Control Experiement Yaw offset considerations

0 0i 0v -15 -30 -5 5 15 35 20 20i 20v -10 -20 -25 -35 30 25 10

Scan sweep ”aligned with nacelle”

30°

50°

• Cyclic toggle sequence of yaw offsets (between ±35 deg) 2D 3D 4D 5D 6D 7D 8D 9D 10D

• Turbine controller scheduled to change target offset every 30-mins

• Also, scan planes are offset by the yaw offset angle. Scan sweep ”aligned with wind”

Corresponds to device alignment with wind direction. 30°

50°

• Enables the full wake to be captured at all yaw offsets. 2D 3D 4D 5D 6D 7D 8D 9D 10D

15 4. Wake Control Experiment Data Processing Steps

Galion raw scans

• Recorded beam-by-beam • Download from Wood Group FTP server Scan Scan Point Binning & Post Processing Filtering Filtering Averaging Processing

• • • • Beam velocity Discard entire Discard individual Bin criteria: yaw • Aggregate scans  scans w. atypical points in scans: wind speed, atm. projections • Wake profiles WTG conditions • Data quality/ SNR stability • Normalization • Wake trajectories 50Hz Time • S-111 wake • Aggregate scans per bin • Wake decay rates SCADA Averaging interference

• Download from • 1-min time-avgs. CS data logger • Min., Max., and • Uncompress Std. Dev. (1-min) logged signals

16 4. Wake Control Experiment Entire Scan Filtering

• Criteria for discarding entire scans – Turbine actively yawing (first 3 minutes after yaw offset toggle time) – Turbine state other than normal operation – S-97 rotor in wake of S-111 turbine. Wind sector from 232 to 304 deg – Specified yaw offset disagreement (WTG controller versus LIDAR) – Large wind speed variations during individual scan (∆>1 m/s start-to-end)

• Goal was to obtain >100 scans per set of conditions – Yaw offset target – Wind speed ranges: 5-8m/s; 8-11m/s; 11-20m/s – Atmospheric stability

17 4. Wake Control Experiment Scan with S-111 wake Point Filtering

• Each scan consists of 21 beams. Measurement gates every 30m • With 1km range: 21x34 = 714 points per scan • Normalization of measured point velocities with S-97 nacelle wind speed (average: scan start/end)

• Criteria for discarding individual points from scan – Data Quality (low return intensity on beam signals). – Clearly unphysical wind speeds Negative ; <5% of nacelle W.S. ; >130% of nacelle W.S. – Points estimated to be within S-111 wake zone (3D distance)

Scan with S-111 wake REMOVED 18 4. Wake Control Experiment

No yaw offset +5deg yaw offset • Wake characterization from aggregated mean scans for different conditions. Individual scan images too patchy

+15deg yaw offset +25deg yaw offset

Horizontal wake profiles (normalized) at hub height Yaw offset = 20deg

19 4. Wake Control Experiment – Wake Deflection

2.0 D • Postprocessing of wake deficit profiles to determine wake center, wake width, and wake deficit. • We find Gaussian fitting of deficit profiles to be most robust approach. • This enables comparison with FLORIS model predictions.

Model underestimation 3.9D of re-direction 3.9D 3.9D

5.1 D Y/D

Yaw 20 4. Wake Control Experiment – Wake Decay

Normalized velocity @ wake center Wake width: σ/D

• Excellent agreement in wake recovery between model • Change in wake width (line slopes) predicted well by and experiment, both at 0 and ±30 deg yaw offsets. model. • There is an offset due to near wake model .

21 • FLORIS wake models validated against measurements.

• On an overall note, models are adequate and can be trusted

• Some model shortcomings were identified and addressed – Turbulence impact on wake redirection – Width from near-wake models does not reflect impact of yaw offset – Power predictions deep inside wind farms have systematic bias

22