Wind Energ. Sci., 2, 229–239, 2017
www.wind-energ-sci.net/2/229/2017/
doi:10.5194/wes-2-229-2017
© Author(s) 2017. CC Attribution 3.0 License.
Field test of wake steering at an offshore wind farm
Paul Fleming
1
, Jennifer Annoni
1
, Jigar J. Shah
2
, Linpeng Wang
3
, Shreyas Ananthan
2
, Zhijun Zhang
3
,
Kyle Hutchings
2
, Peng Wang
3
, Weiguo Chen
3
, and Lin Chen
3
1
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO 80401, USA
2
Research & Development, Envision Energy USA Ltd, Houston, TX 77002, USA
3
Research & Development, Envision Energy Ltd, Shanghai, 200051, China
Correspondence to: Paul Fleming (paul.fleming@nrel.gov)
Received: 16 January 2017 – Discussion started: 6 February 2017
Revised: 4 April 2017 – Accepted: 7 April 2017 – Published: 8 May 2017
Abstract. In this paper, a field test of wake-steering control is presented. The field test is the result of a col-
laboration between the National Renewable Energy Laboratory (NREL) and Envision Energy, a smart energy
management company and turbine manufacturer. In the campaign, an array of turbines within an operating
commercial offshore wind farm in China have the normal yaw controller modified to implement wake steer-
ing according to a yaw control strategy. The strategy was designed using NREL wind farm models, including
a computational fluid dynamics model, Simulator fOr Wind Farm Applications (SOWFA), for understanding
wake dynamics and an engineering model, FLOw Redirection and Induction in Steady State (FLORIS), for yaw
control optimization. Results indicate that, within the certainty afforded by the data, the wake-steering controller
was successful in increasing power capture, by amounts similar to those predicted from the models.
1 Introduction
Wind farm control is an active field of research in which the
controls of individual turbines co-located within a wind farm
are coordinated to improve the overall performance of the
farm. One objective of wind farm control is improving the
power production of wind farms by accounting for the wake
interactions between nearby turbines.
In one wind farm control concept, turbines are yawed to
introduce a deflection of the wake away from downstream
turbines. This method has been referred to as “controlling
the wind” (Wagenaar et al., 2012) and “yaw-based wake
steering” (Fleming et al., 2014b). High-fidelity simulations
of wake steering have shown the potential of this technique.
Jiménez et al. (2010) used computational fluid dynamics
(CFD) simulations to demonstrate the wake deflection ca-
pability of wind turbines and provided a model of this de-
flection. In Fleming et al. (2014b), they used National Re-
newable Energy Laboratory (NREL)’s CFD-based Simula-
tor fOr Wind Farm Applications (SOWFA) to investigate the
capabilities of wind turbines to redirect wakes. In Vollmer
et al. (2016), the behavior of wake steering in different atmo-
spheric conditions was investigated, also using CFD. Finally,
in Fleming et al. (2014a), simulations of two-turbine wind
farms, again using SOWFA, were used to show that through
wake steering, the net power of the two turbines is increased
when the upstream turbine applies an intentional yaw mis-
alignment.
Based on high-fidelity simulations, there appears to be
good opportunities for improved power performance of wind
farms with significant wake losses. Recent efforts have fo-
cused on the design of lower-fidelity, controller-oriented
models, and controllers based on these models that use
wake steering to actively improve power. In Gebraad et al.
(2014), the FLOw Redirection and Induction in Steady State
(FLORIS) model is described and used to determine optimal
yaw settings for a model six-turbine wind farm. Set points
for a particular wind speed and direction are determined by
optimizing the yaw angles of the turbines using FLORIS, and
these set points are used in SOWFA simulations. The results
from SOWFA agree with the predictions from FLORIS, and
total power capture is increased by 13 %. This work is car-
ried further, and FLORIS is used to assess the overall im-
provement from control, first for one speed and over a wind
Published by Copernicus Publications on behalf of the European Academy of Wind Energy e.V.
230 P. Fleming et al.: Field test of wake steering at an offshore wind farm
rose of directions in Fleming et al. (2015) and then to deter-
mine the overall annual energy production in Gebraad et al.
(2016). These studies indicate a good potential for improved
overall annual power production for wind farms experiencing
significant wake losses. It should be noted that even greater
benefits can be yielded if future wind farms are designed for
active control of wakes, rather than using large inter-turbine
spacings to avoid wake losses. This combined optimization
of wind farm control and wind farm system engineering is
a subject of active research. These simulation studies have
demonstrated a theoretical potential of wind farm control.
However, it is often noted that issues arising in implementa-
tion in real conditions might undermine the positive results.
Inaccuracies in the control-oriented models or high-fidelity
simulations have been cited as a potential issue. Additional
modeling of constantly changing wind direction could im-
prove the comparison between simulation and field testing.
Some field testing of wake steering has been performed
to date to understand the potential of this wind farm control
strategy. In Wagenaar et al. (2012), wake steering is imple-
mented at a scaled wind farm; however, the results are incon-
clusive. Wind tunnel testing of wake steering is performed in
Schottler et al. (2016) and Campagnolo et al. (2016), and the
results are encouraging because in each case that wake steer-
ing is observed, overall power capture is improved for two-
turbine cases. The results are in alignment with earlier sim-
ulation studies conducted by Jiménez et al. (2010), Gebraad
et al. (2014), and Fleming et al. (2014a) in the amount of im-
provement and the asymmetric relationship of yaw misalign-
ment and power improvement. On the full scale, a nacelle-
mounted lidar is used to observe wake deflection on a utility-
scale turbine in Trujillo et al. (2016). Finally, in Fleming et al.
(2016), two ongoing studies of wake steering are presented,
which are both part of a multiyear US Department of Energy
Atmosphere to Electrons project. In one of the field studies
at the Scaled Wind Farm Test Facility (SWiFT), two 27 m
diameter (D) turbines (intentionally aligned in the dominant
wind direction at a spacing of 5 D) were used to perform a
comprehensive test of wake steering. A second ongoing ex-
periment was done, in which a rear-mounted lidar was used
to monitor a wake of a utility-scale turbine, which is set to
hold a specific yaw misalignment for a prolonged period of
time. Initial results from both campaigns are in accord with
the model predictions described earlier.
In the present study, a wind farm controller that performs
wake steering is designed and implemented for an operat-
ing commercial offshore wind farm in China with Envision
turbines. The control strategy is simple and based on the ap-
proach used to control the simulated wind farm in Gebraad
et al. (2014). The test was run for several months and data
were collected and compared from time periods when the
controller was operating and not operating. The results and
analysis indicate a successful improvement in power produc-
tion. Additionally, the data provide important validation of
the models, specifically SOWFA and FLORIS, used in the
Figure 1. This figure demonstrates the workflow of this project.
In particular, this project started by running CFD models of the
turbines in this field study to understand the wake characteristics.
The FLORIS model was tuned to those CFD simulations. A control
look-up table was generated that contained the optimal yaw settings
for the control turbine. A field test was conducted with the controller
on and off. The data were analyzed and the results are presented in
this paper. LUT – look-up table; SCADA – supervisory control and
data acquisition.
design of the controller, and can be generalized to similar
CFD and control-oriented models. There are some qualifica-
tions to these results that will be fully considered in the text.
The contributions of this paper include the results and
analysis from a wind farm test of wake-steering control. The
project is a collaborative project between the NREL and En-
vision Energy, a smart energy management company and tur-
bine manufacturer. The positive results motivate further ef-
forts into the design and development of such control. Ad-
ditionally, this paper provides evaluation on the performance
of wind farm control modeling tools in their ability to predict
the effect of wind farm control strategies.
2 Project overview
The goal of the project was to implement a test of yaw-based
wake steering at an operating wind farm. The project was
broken down into a workflow illustrated in Fig. 1, which
shows the stages of work and the structure by which this pa-
per is organized.
The first stage of work is the selection of a wind farm for
use in the experiment. The Longyuan Rudong Chaojiandai
offshore wind farm in Jiangsu, China, consists of turbines
from multiple manufacturers undergoing various phases of
construction. A selected portion of the site was studied for
this effort, consisting of 25 Envision EN136/4 MW turbines
incorporating a high-speed three-stage gearbox and induc-
tion generator (see Fig. 2a).
From the wind farm, a subset of turbines was selected for
implementing the experiment. These turbines form the front
two rows of turbines for winds coming from the northeast.
The arrangement of these turbines and their names to be used
throughout this paper are shown in Fig. 2.
For the campaign, a single turbine was selected to be the
controlled turbine. This is turbine C1, indicated in Fig. 2.
The turbine is shown to wake three particular turbines: D1,
from a wind direction of 340
◦
at a distance of 7 D; D2, from
a wind direction of 51
◦
at a distance of 8.6 D; and D3, from
a wind direction of 81
◦
at a distance of 14.3 D. A control
Wind Energ. Sci., 2, 229–239, 2017 www.wind-energ-sci.net/2/229/2017/
P. Fleming et al.: Field test of wake steering at an offshore wind farm 231
Figure 2. (a) Rudong wind farm used in the field study. (b) Turbine locations. The wake-steering control strategy is implemented to mitigate
the wake interactions between C1, on the one hand, and D1, D2, and D3, on the other. As indicated by the diagram, C1 is the control turbine,
R1 is the reference turbine, and D1, D2, and D3 are downstream turbines waked by C1. Turbines O1 and O2 are other turbines not directly
used in the study but whose wakes are noticed in certain directions.
strategy was designed to improve the summed power of tur-
bine C1 and a downstream turbine (i.e., C1 and D1, C1 and
D2, and C1 and D3) through yaw misalignment of turbine
C1. This approach provides three tests of wake steering at
different inter-turbine distances. Of these, the pairing with
turbine D1, at 7 D, is most promising. This is the distance
used in Fleming et al. (2014a), which showed a potential net
improvement of 4.5 % in power capture in a study with the
NREL 5 MW reference turbine for the waking wind direc-
tion. Finally, a reference turbine which is not waked in any
of the experimental directions is chosen to provide reference
signals. This is turbine R1 in Fig. 2.
In the initial phase of work, Envision shared with NREL
a FAST model of the turbines (FAST is an aero-servo-elastic
wind turbine simulation tool maintained by NREL; Jonkman
and Buhl Jr., 2005). Additionally, Envision provided the lay-
out of the turbines used in the campaign. Finally, Envision
provided several months of supervisory control and data ac-
quisition (SCADA) data used to evaluate and tune models in
advance of the campaign. As shown in Fig. 1, these inputs
were then included in the derivation of the models used in
the yaw control strategy, which will be described in the next
section.
3 Modeling
3.1 SOWFA
The first model to be used in this study was the SOWFA
model (Churchfield and Lee, 2014). SOWFA is a wind farm
simulation tool, which models the atmospheric boundary
layer using CFD and then models the turbines using embed-
ded FAST models (in the version of SOWFA used in this
work). The turbines and flow interact through a two-way ac-
tuator line coupling.
SOWFA has been validated against wind farm SCADA
data (see, for example, Churchfield et al., 2012). However,
validating SOWFA is not the focus of this campaign. The
primary use of SOWFA in this study is generating data sets
of wakes simulated from the Envision turbine, which can be
used to tune the FLORIS model (and repeating the proce-
dure that was done for the NREL 5 MW reference turbine in
Gebraad et al., 2014).
Using the FAST model of the turbine, a suite of simula-
tions is assembled. A wind condition of 8 m s
−1
wind and
6 % turbulence (Gebraad et al., 2014) was used for this study.
Simulations were then run with a single turbine operating
with various amounts of yaw misalignment, as well as for
two turbine cases, wherein the upstream turbine has various
yaw misalignments and the downstream turbine is placed in
various positions downstream and cross-stream. The trends
in power for the single-turbine case and two-turbine case pro-
vide important data with which to tune the control-oriented
FLORIS model, described in the next section.
3.2 FLORIS
Using a FAST model of the turbine and the data sets pro-
duced by SOWFA, it is possible to obtain a FLORIS model
that can predict the power of turbines in a farm in steady
state, including wake redirection. As discussed in Gebraad
et al. (2014), the FLORIS model is primarily based on
the Jensen model (Jensen, 1984) and the Jiménez model
(Jiménez et al., 2010). In particular, FLORIS identifies three
different wake zones with separate wake recovery parame-
ters to capture the wake characteristics. In addition, FLORIS
includes the Jiménez model to incorporate the wake deflec-
tion caused by yaw misalignment. FLORIS contains a set of
parameters to be tuned for a given turbine, including param-
eters describing the turbine and wake behavior.
Some parameters of the FLORIS model can be set directly
using the FAST model. Examples of this include the rotor ra-
dius and the table of power and thrust coefficients by wind
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232 P. Fleming et al.: Field test of wake steering at an offshore wind farm
Table 1.
FLORIS wake tuning.
Parameter NREL 5 MW Envision 4 MW
k
d
0.17 0.26
k
e
0.05 0.063
W D
init
−4.5
◦
−3.7
◦
pP 1.88 1.43
speed, which can be obtained by running steady wind simu-
lations in FAST.
The remaining parameters are tuned in this work through
an optimization routine that minimizes the error between the
power outputs simulated in SOWFA and those predicted by
FLORIS. As a design rule, the smallest set of parameters
should be adjusted away from their default settings that pro-
duce a reasonable fit. Through iteration, it was determined
that adjustments to four parameters gave a good approxima-
tion. The parameters that we focus on are described below.
The parameter pP relates yaw misalignment to reduction
in power by
L
yaw
= cos(γ )
pP
, (1)
where γ is the yaw misalignment of the turbine and L
yaw
is
the fraction of power relative to a non-yawed baseline. The
higher pP is, the more quickly a turbine loses power by mis-
alignment and the less likely it is that wake steering will work
because the downstream turbine needs to recover more power
to compensate for power lost by the upstream turbine.
The parameter k
e
determines the rate at which a wake ex-
pands and recovers to the free-stream velocity. A larger k
e
value indicates a faster wake recovery to free stream. Stan-
dard values in the literature range from 0.05 to 0.1. Simi-
larly, k
d
, based on the Jiménez model, describes the rate at
which a deflected wake reverts to the free-stream direction.
A larger k
d
parameter indicates that the wake is less sensitive
to yaw misalignment. Standard values in the literature range
from k
d
= 0.1 to k
d
= 0.3. Finally, W D
init
describes an initial
wake deflection angle without steering, which is important
for capturing the asymmetry of wake steering. This asym-
metry is likely caused by the combination of the rotation of
the turbine and the shear layer in the atmospheric boundary
layer.
The results of the tuning optimization are presented in Ta-
ble 1, which shows both the default values obtained from tun-
ing to the NREL 5 MW reference turbine as well as the newly
obtained values.
Among these, the lower value of pP is interesting, as 1.43
is below the value obtained for the NREL 5 MW turbine
(1.88) and other experimental results. For example, the co-
efficient is fit to wind tunnel tests in Medici (2005) to be 2.0.
However, 1.43 is an attractive number as it implies that wake
steering can be performed with less losses incurred on the
upstream yawed turbine.
4 Control design and field test
Given a completed FLORIS model, it is now possible to de-
rive a set of yaw misalignments for turbine C1 that will opti-
mize power for the pairs of turbines (D1, D2, and D3 down-
stream) by wind direction. Wind speed is not used as an input
as inspection indicated minimal sensitivity. It is important to
note that there is not much benefit at very low and very high
wind speeds, suggesting that it is sufficient to enable and dis-
able the controller by wind speed rather than scheduling.
Some constraints were placed on the optimization. First,
for turbine loading and safety reasons, the maximum yaw
misalignment was limited to 25
◦
. Second, it was decided
for this experiment to limit the controller to positive yaw
misalignment angles (in our nomenclature this is rotating
the turbine counterclockwise from the wind when viewed
from above). This is because this has been demonstrated to
be more effective (see, for example, Fleming et al., 2014a,
where positive yaw misalignments yield higher power in-
creases as compared to negative yaw misalignments), and
including negative misalignments might raise loads and re-
quire a nontrivial transition from positive to negative yaw
misalignments near the wake crossover point, i.e., when it
becomes more beneficial to redirect the wake from the right
side of the downstream turbine to the left side of the down-
stream turbine. Using the tuned FLORIS model and these
constraints, it is possible to now derive an optimal table of
yaw misalignments for turbine C1. This is shown in Fig. 3.
The optimal yaw misalignment angles for turbine C1 are
shown in the upper left of Fig. 3. The dashed lines indicate
the directions in which turbine C1 wakes one of the three
downstream turbines (see Fig. 2). Near the fully waked di-
rections, the misalignments are largest and taper down as less
deflection is needed to remove the partial wake overlap sit-
uations. The power loss of turbine C1, shown in normalized
power, is indicated in the middle left plot. The overall “plant”
gain for these four turbines is then shown in the lower left. Fi-
nally, the right panels show the normalized power of the three
downstream turbines with and without wake steering. Based
on the percent improvement, the sum power is expected to in-
crease for all three pairings, meaning the gains downstream
exceed the losses upstream. However, this is least so for tur-
bine D3 as at 14.3 D, the baseline wake loss is much less,
which is expected.
Using this table of offsets by wind direction, engineers at
Envision modified the yaw controller of turbine C1 to de-
liver these offsets. Note that this offset tracking must be done
within the limits of yaw control actuation, and for safety
reasons the offset was disabled in sustained winds above
10 m s
−1
. We note here also that the purpose of the experi-
ment was to demonstrate the principle of wake steering and
not produce a fully optimized closed-loop control implemen-
tation, which is expected to be the subject of future work. As
will be discussed, the present controller offsets correctly in
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P. Fleming et al.: Field test of wake steering at an offshore wind farm 233
Figure 3. FLORIS optimal yaw misalignment results. The dashed vertical line indicates the direction in which the downstream turbine is
fully waked by turbine C1. See Fig. 2 for details on the precise wind directions. Specifically, the first dashed line (from left to right) refers to
the direction of turbine C1 fully waking downstream turbine D1. The second dashed line refers to the direction of turbine C1 fully waking
downstream turbine D2. Finally, the third dashed line refers to the direction of turbine C1 fully waking downstream turbine D3. Wakes from
turbines other than C1 are also evident from the dashed lines. Note that for the power subplots, the power is given normalized to the power
of the turbine in un-yawed and un-waked conditions
Figure 4. Cosine exponent fit. Panel (a) shows the range of data over various yaw misalignments and the power ratio. The blue line indicates
a cosine function with the fit exponent value of pP = 1.41, and the banded region indicates the confidence interval as described in the text.
Panel (b) indicates the number of points included in each yaw alignment position.
the average sense, with a wide variation of offsets occurring
dynamically.
Following the implementation of the controller, the exper-
imental campaign was then run in two phases. In the first
phase, the wind farm was operated normally while data rel-
evant for this campaign were collected. This phase lasted 4
months, from 3 April 2016 to 5 August 2016. The second
phase used the controller designed above and was run an ad-
ditional 4 months, from 5 August 2016 to 2 December 2016.
It is regrettable that the campaign could not be run longer.
Naturally, only a portion of the data features the wind di-
rections of primary interest. Additionally, it should be noted
that it would be better to alternate the controller on and off
regularly throughout the campaign to better compare condi-
tions when the controller is on than when it is off. However,
Rudong is a commercial wind site with internal and external
restrictions, and it was necessary to run the test within these
constraints. It is important to note that there are limitations
with field testing and data collection. In particular, the re-
sults are impacted because of the relatively short window of
data collection. Further, the sequential testing pattern opens
the possibility of confounding influences such as seasonal
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