The energy transition means that more and more wind farms are being built in
favorable offshore sites like the North Sea. The wind farms affect each other
as they interact with the boundary layer flow. This phenomenon is a topic of
current research by the industry and academia as it can have significant
technical and financial impacts. In the present study, we use data from the
Alpha Ventus wind farm site to investigate the effects of inter-farm
interactions. Alpha Ventus is the first offshore German wind farm located in
the North Sea with a fully equipped measurement platform, FINO1, in the near
vicinity. We look at the effects on the wind conditions measured at FINO1
before and after the beginning of operation of the neighboring farms. We show
how measured quantities like turbulence intensity, wind speed distributions,
and wind shear are evolving from the period when the park was operating alone
in the area to the period when farms were built and operate in close
proximity (1.4–15 km). Moreover, we show how the wind turbine's
response in terms of loads and generator and pitch activity is affected using
data from a turbine that is in the vicinity of the mast. The results show the
wake effects in the directions influenced by the wind farms according to their
distance with increased turbulence intensity, reduced wind speeds, and
increased structural loading.
Introduction
The reduction of produced emissions and the transition to renewable energy
sources require a large increase in the installed capacity of wind. This leads
to more and larger wind farms being built with ever-increasing turbine
sizes. Offshore sites have a lot of benefits compared to onshore – i.e., higher
wind speeds with lower turbulence, higher social acceptance, larger space
availability, and larger energy density due to the possibility to install
larger machines. Thus, offshore sites with favorable wind, soil, and depth
conditions, like the North Sea, are being populated with wind farms that have
to be spaced as close as possible.
Similar to single wind turbine wakes, the wind farms as a whole interact with
the atmospheric boundary layer and create wakes that are propagated downstream
. This phenomenon is more prominent in offshore farms
where the machines used are larger, the ambient turbulence intensity (TI) is lower,
and the surface roughness is lower than onshore sites. These effects need to
be modeled and considered when planning the siting of wind farms as they can
have a large impact on the operating conditions experienced by the neighboring
wind farms. Neglecting such effects can lead to large deviations in the annual
energy production (AEP) estimates, as well as the lifetime of the structural
components .
Therefore, understanding these inter-farm interactions has been a topic of
interest for research, as well as the industry. Previous studies based on
airborne measurements have identified wind
speed deficits and increases in turbulence downstream of offshore wind farms
and clusters. They showed that these effects are visible up to a level of
50 km downstream, especially in neutral atmospheric conditions. In
, measurements with synthetic aperture radars (SARs) and
Doppler radars were performed showing a speed deficit of 4 % to
8 % up to 10 km downstream of the farms. The work of
used also SAR measurements to investigate wind farm
wakes in the North Sea and the Baltic Sea. They reported a speed deficit in
the near wake, with full recovery at distances of 5–20 km depending
on the free-stream wind speed, the atmospheric stability, and the number of
operating turbines. In the study of , turbine data were
used to identify the magnitude of the wake effects. They reported a detectable
wind speed deficit and wake-induced turbulence due to neighboring farms at a
distance of 4.2 km (38.8 D). In long-range
lidar and satellite SAR measurements were used to investigate inter-farm wake
effects for wind farms spaced at distances of 24 and 55 km. They
identified wind speed deficits for the downstream farm to be higher in stable
and weakly unstable atmospheric conditions, with mean levels of 20 %
and 25 % for distances of 24 and 55 km, respectively. In
data from the FINO1 platform are analyzed cumulatively to
examine the wind speed and turbulence intensity distributions. The different
periods of the Alpha Ventus (AV) wind farm project – preconstruction, single farm
operation of Alpha Ventus, and cluster operation with the surrounding farms –
are examined based on mean cumulative values and time series. The results show
that the mean wind speed decreased by 1 ms-1 over the periods, and the mean turbulence
intensity increased from 5.5 % to 8.5 % due to the farm
wakes from the neighboring farms.
Moreover, numerical studies have been carried out to understand the underlying
mechanisms of wind farm wakes. In large eddy simulations were
performed to correlate atmospheric stratification, farm size, and layout with
the flow inside and around a wind farm. They identified velocity deficits at a
level of 3.5 % compared to free stream values at a 10 km
distance for small vertical temperature gradients (1 Kkm-1) with
the wake-induced turbulence propagating up to 10 km. Moreover, for a
higher gradient of 5 Kkm-1, they report a full wake recovery at a
distance of 5 km. studied the effects of large wind
farms on the atmospheric boundary layer, showing that it is affected due to
the enhanced mixing caused by the wakes. In a variety of
numerical models, with ranging fidelity, were compared against supervisory control and data acquisition (SCADA)
measurements for predicting the wake effects in a cluster of two wind farms
separated by a distance of 3 km (30 D). The operational and wind
data used were not of sufficient quality and quantity to draw meaningful
conclusions. Nevertheless, the widespread results between the numerical models
showed the need to further investigate the current capabilities and
reduce the inherent uncertainty.
The need to include these effects in practical engineering studies has led to
the development of engineering wind farm wake models with varying fidelity and
requirements. suggested a simple analytical model to
calculate the speed deficit accounting for atmospheric stability, surface
roughness, the turbine's thrust coefficient, and the Monin–Obukhov
length. Calibration and evaluation of the model can be found in
. In an engineering model accounting for
both speed deficits and wake-induced turbulence is suggested. It is based on
the Jensen–Katic model , extended to include wake-induced
turbulence and validated against measurements for power production.
In this context, the goal of the present work is to investigate the effects of
inter-farm wakes by analyzing measurement data. More specifically, metocean
data from the FINO1 measurement platform along with SCADA and loads data from
the closest machine of Alpha Ventus (AV) to the mast are analyzed for the
period 2010–2019. Until 2015, AV was the only wind farm operating in the
area. This setup allows us to observe how the metocean conditions – as
measured by FINO1 – have changed in relation to the surrounding wind farm
construction and how these changes impact the turbine's response at AV. The
data are analyzed per direction and wind speed on an annual basis. This will
give insight to researchers working on modeling the inter-farm interactions,
as well as to practitioners focusing on optimizing wind farm siting and
planning. Furthermore, the data presented here can be used by researchers
doing research related to AV in order to represent the metocean conditions
during the different operational periods of the project.
The rest of the paper is structured as follows: in Sect. 2 we describe the
site and the locations of the farms and the met mast around the Alpha Ventus
site. In Sect. 3 the measurement equipment and the data processing methods are
discussed. Section 4 presents the results in terms of metocean conditions and
turbine responses, followed by a discussion on the findings in Sect. 5.
Site description
Alpha Ventus is the first German offshore farm commissioned
in 2010 and is located in the North Sea close to the island of Borkum. It
consists of 12 fixed bottom wind turbines with a rated power of
5 MW. Half of the turbines are manufactured by REpower (renamed to
Senvion) and have a jacket support structure, and the rest are manufactured by
Adwen and use a tripod substructure. The research projects at AV are supported
by the initiative Research at Alpha Ventus (RAVE) by
coordinating the research activities and providing measurement data.
FINO1 is a research measurement platform including a
fully equipped met mast erected in 2004 in the North Sea. It is located close
to AV, at a 400 m (3.2 D) distance from the AV4 turbine. The data from
both FINO1 and AV are processed and made available in one database
operated by the Federal Maritime and Hydrographic Agency
(BSH). Data have been collected since the pre-construction phase (2004–2010)
of AV and also during the operational phase from 2010 to today.
As it was initially planned, new wind farms have been built around AV over
time, operating at different distances and in different directions. Today, AV can be seen
as a part of a larger wind farm cluster consisting of five wind farms equipped
with similarly sized turbines. This allows us to observe how the site
conditions have evolved during the years due to the interannual variability
but also due to the presence of the neighboring wind farms.
An overview with all the wind farm information, the distances, and the
directions relative to FINO1 are given in Table . A
sketch intended to give the reader an impression of the topology of the area
is given in Fig. . In 2015, two new wind farms started operating
in the area. The closer one, Borkum Riffgrund 1 (BR1)
, is located southwest of AV at distances between
2.8 and 7.6 km from FINO1, and the relevant directions are 70 to
244∘ (considering FINO1 as the origin of a clockwise system with north
pointing up at 0∘). BR1 consists of 78 turbines of 4 MW with a
rotor diameter of 120 m. The second, Trianel Borkum 1 (TB1)
, is located northeast of AV at distances between 6 and
14 km and 253–315∘ direction relative to FINO1. The total
installed capacity is 200 MW consisting of 40 turbines with rotor
diameters of 116 m.
At the beginning of 2019, the Merkur (MRK) wind farm
was commissioned. Merkur is the closest farm to FINO1 at relative distances
between 1.4 and 8 km and with a relevant azimuth sector of
235–45∘. MRK consists of 66 turbines rated at 6 MW with
150 m rotor diameters and a total capacity of 396 MW. The
second part of the Borkum Riffgrund project, the Borkum Riffgrund 2 (BR2) wind
farm, started operating in mid-2019. It is the largest wind farm in the area with
448 MW capacity consisting of 8 MW machines with 164 m
rotor diameters. The relevant directions to FINO1 are 80–250∘ at
distances between 9 and 13 km. Finally, in 2020 the extension of the
Trianel Borkum project, the Trianel Borkum 2 wind farm, started operating, but it is
not in the scope of this study as we use data up to 2019.
Most of the information (e.g., construction/commissioning dates, geographic
locations) shown here are gathered from publicly available sources which could
not be fully verified. As is also explained in the following section
regarding the data, some uncertainties exist due to imprecise information on
dates, coordinates, maintenance logs, etc., which led us to be more
conservative on the data filtering and to consider them in the interpretation of
the results.
Layout of Alpha Ventus, FINO1, and the surrounding wind farms
including relative directions and distances. The origin is the FINO1
platform. Background layer used with permission from
4C Offshore (last access: 1 December 2020).
Overview of the wind farms at the site and the relevant distances and azimuth directions to FINO1. WT stands for wind turbine, and OEM stands for original equipment manufacturer.
In this section we present the measurement equipment used, the data
filtering and calibration approaches used, and the post-processing methods applied
to obtain the results shown in the next section. All data were obtained
through the RAVE database which is publicly available
under user agreements. The data used in this study were from the period 1 January 2011–31 December 2019.
The metocean data were collected from the FINO1 platform. The data were
provided as 10 min statistics with corrections and calibration values
applied as instructed by the providers to compensate for effects like mast
shadowing (see also , and
). As these data are commonly used in the
research community, we do not discuss all the sensors available and the
specifics of the setups here. A thorough overview of the database and data
quality considerations can be found in . In the study
of , a comprehensive database of aggregated fitted
distributions for different quantities is presented for the period
2004–2016. These data have been also used for examining the influence of
stability on the loading spectrum of the Alpha Ventus turbines
, as well as for the validation of aeroelastic farm-wide
simulation tools .
For the wind conditions, we used the cup anemometers mounted at heights from
30 to 100 ma.s.l. (above sea level) at increments of
10 m. The wind speed and turbulence intensity (TI) measurements were
taken from the top anemometer at 101.5 m. This is higher than the
92 m hub height of the Alpha Ventus turbines but was selected since
there are fewer mast shadowing effects in the directions of interest
more information on this can be found
in. The data were corrected for shadow effects
and calibration according to communication with the data providers and as
explained in . The wind shear was
calculated from all available heights assuming a power law exponent. The shear
exponent α was fitted to the data by minimizing the least squares
difference with the measured values. The wind direction statistics were
obtained by the wind vane at 90 m with the relevant corrections
applied as discussed in . The temperature and
pressure data were also obtained as 10 min statistics. We used the
thermometers on the mast at 34 and 52 ma.s.l., and the pressure was
obtained by the barometer at 21 ma.s.l. The oceanographic data used
here were also 10 min statistics and included sea surface temperature
from the buoy and significant wave height (Hs) and peak period (Tp) from
the radar mounted on the platform.
The turbine data used come from the AV4 turbine which is manufactured by
Senvion and has a rated power of 5 MW, a hub height of 92 m,
and a rotor diameter of 126 m. It is located at 92∘
azimuth relative to FINO1 at a distance of 3.2 D. The data were provided at a
50 Hz sampling rate. For the tower base loads, we used the strain
gauges located above the transition piece and combined them with the nacelle
yaw position signals to derive the fore–aft loads. Moreover, from the SCADA
signals, we obtained the nacelle yaw position, blade pitch angle, generator
power, and generator speed. For the nacelle yaw position sensor, a slow drift
over time was observed. This was corrected by using events in which the turbine
was not operating and the nacelle was rotated 360∘. We correlated
the known locations of the strain gauges around the tower with the nacelle yaw
signal in order to derive corrections of the offset and compensate for the
sensor drift.
In order to filter the metocean statistics data we applied multiple
criteria. The quality flags provided by the database were used. Data blocks
with availability of less than 90 % for the 10 min period
were rejected. Appropriate thresholds for minimum, maximum, and standard
deviation values were implemented as an additional filtering criterion. More
specifically, for the wind speed we accepted measurements with mean values
between 1 and 30 ms-1, minimum value above 0.5 ms-1,
maximum value below 40 ms-1, and standard deviation below
40 %. Similarly, for TI we accepted values between 1 % and
40 %. For the high-resolution turbine data, we applied the same
filters for availability and statistics as before. Additionally, we only
accepted events for which the nacelle yaw position and the met mast wind direction
had a difference smaller than 3∘ to avoid including events for which
the turbine was misaligned with the mast. Similarly, we rejected events for which
the nacelle position signal's standard deviation was higher than
5∘ and events for which the difference between the maximum and
minimum values was higher than 5∘ to avoid including the effect
of yawing in the calculated loads. Furthermore, there were many periods in which
the turbine was operated with curtailed power which affects loads (see, for example, ). We filtered out these periods by applying a
filter involving combined thresholds for pitch angle, generator speed and
power, and wind speed to make sure that the machine is operated invariably in
full power. Turbine data from 2019 were not available to us at the time of
this study.
For the post-processing of the loads, we used the rainflow algorithm and
Miner's rule with a Wöhler exponent of 4 and a reference cycle number of
600 to derive the 1 Hz damage equivalent loads (DELs). Due to the
non-disclosure agreement with the turbine manufacturer, the DEL values shown
are normalized with values close to the maximum. For the wind speed
distributions, we binned the data and fitted the Weibull parameters k and C
using least squares. In all the plots that include bands, they represent the
15th and 85th percentiles. Finally, we do not show the scatter but only the
mean value and the band to avoid congestion and improve clarity for the
reader.
Regarding the periods of measurements, we decided to use full years for all
the quantities. This was done to facilitate the analysis of the wake effects
which is the main objective of this study and also to avoid bias due to
seasonal variability. To achieve this, we rejected measurements that had large
continuous gaps in the year and also years with less than 80 %
availability in total. In other analyses that are based on binning
(e.g., directional or wind speed bins), we accepted only bins that included
25 points or more to avoid statistical bias.
There are some known sources of uncertainty affecting the results shown
here. We do not have access to the service log of the turbines; hence, we are
not aware of maintenance or replacement activities on the turbine components
(or measurement devices) that might have influenced the results. We tried to
compensate for these with the filtering approaches mentioned earlier. We also
do not have access to operational information of the neighboring farms in order
to know when the farms might have stopped operating or when they were
curtailed, which is expected to have an impact on the wake effects. Finally,
availability varies significantly from sensor to sensor. Especially in turbine
data for which the filters required a combination of sensors, the resulting
availability was much lower.
Another known uncertainty comes from the measurements relevant to the 30–190∘ directions. There, FINO1 is in the direct wake of Alpha
Ventus. Thus, we should not consider them as free-stream directions and should take
into account that wind speeds are probably underestimated and TI is
overestimated compared to the real free-stream wind speeds seen by Alpha
Ventus in this sector. Nevertheless, this sector is useful for our analysis as
there have been no changes in the surroundings since AV started
operating. Thus, this sector can be used as a validation sector influenced
only by the interannual variability and the measurement uncertainties but not
by wake-related effects.
Results
The results from FINO1 and the AV4 turbine will be presented in two
sections. The first is dedicated to metocean conditions and the second to the
turbine's response. We are going to analyze these in terms of azimuth
directions, as shown in Fig. . This figure can be used as a
reference for the reader for the positioning of the farms and the azimuth
sectors relative to FINO1, which is used as the origin. Additionally, we use a
color code with shades of blue for the years when Alpha Ventus was the only
operating farm in the area (2011–2014) and shades of red for the years
2015–2018 when the TB1 and BR1 farms were also operating. The yellow color
refers to the year 2019 when MRK and BR2 wind farms started operating too.
We will focus the analysis on the 200–320∘ sector as it is the
relevant one for investigating the inter-farm wakes. In the 30–170∘ direction the mast measurements are directly influenced by the
wakes of AV. The directions around 180 and 360∘ are influenced by
the mast's own shadowing as previous studies have also shown see, for example,. Hence, we decided to exclude these sectors
which also have a low probability of occurrence.
Azimuth sectors of focus and wind farm orientation
layout. Background layer used with permission from
4C Offshore (last access: 1 December 2020).
Metocean conditions
Initially, an overview of the probability of occurrence of wind directions and
wind speeds at the site can be seen by looking at the cumulative wind rose
including all periods in Fig. . The dominant
directions are in the sector 200–330∘ with more than
58 % probability, while the highest wind speeds are observed in the
southwest sector. This principal sector is the one potentially affected by the
neighboring farms, and we are going to focus our analysis on it. The
measurements in the eastern directions (30–170∘) are heavily
influenced by the wake of Alpha Ventus itself and are expected to
underestimate the wind speed magnitude. The probability of occurrence for this
sector is about 25 %.
Wind rose from FINO1 cup anemometers and wind vane at 90 m. All periods are considered.
We analyzed the annual wind speed magnitude probability by looking at the
fitted Weibull distributions. In Fig. we show the annual
distributions, considering all the wind directions, along with the relevant
IEC standard design class of the AV4 turbine (class II). The
years 2015 and 2018 were not used as there were significant data gaps that
could cause seasonal bias (15 % and 40 % missing data,
respectively). The removal of 2015 was also decided because this is year that
TB1 and BR1 started operating in different periods (we could not verify the
exact dates), leading to a change in the measured conditions during the
year. The differences between the years 2011–2014 are attributed to the
interannual variability of the weather conditions. In the years 2016 and
2017, we observe a shift in the distributions towards lower wind speeds due to
the operation of TB1 and BR1. In 2019 there is a further reduction in the wind
speeds due to the operation of MRK and BR2. The expected deviations due to
interannual variability are smaller compared to the
deviations due to the wakes, and a pattern of shifting towards lower speeds is
observed. Additionally, long-term studies of interannual variability in the
North Sea, combining several locations and measurement campaigns, have shown
that it is lower than 4.5 % (see, for example, ). The IEC
class II distribution suggests higher wind speeds compared to all measured
periods. As mentioned earlier, all the measured distributions are expected to
be slightly underestimated due to the sector influenced by the wake of AV
(30–170∘). This is not expected to influence the relative
differences over the years since it has a low probability of occurrence and
can be seen as an offset influenced only by the interannual variability.
Fitted Weibull distributions of wind speed magnitudes per year including all directions.
In Table we show the fitted Weibull coefficients and
the calculated theoretical AEP for a single turbine operating in these
conditions. The theoretical AEP is simply derived by summing the product of
the discreet approximations of the theoretical power curve of the NREL 5MW reference wind turbine with the fitted Weibull
distribution over all wind speed bins. It is not derived by power measurements,
and it is only used to give an idea of what the reductions in wind speed would
mean in terms of power production.
Looking at the distribution's scale parameter C, correlated to the
characteristic wind speed, we observe the reduction in the years 2016–2019
compared to the earlier period. The mean value is reduced by 15 %
between the two periods. The reduction is much larger than what is seen for
the interannual variability (about 5 %) and can be attributed to the
inter-farm wake effects. As a consequence, the mean theoretical AEP is reduced
by 19 %, with the reduction being higher in 2019 when MRK and BR2 were
also operating and reaching a level of 25 %. The standard deviation of
the AEP is also slightly increased in the later period. The class II predicted
AEP is also shown as a reference for what is expected from the site, although
pre-construction site-specific values would be more realistic. These results
show that there can be a significant financial impact due to wind speed
reductions from farm wakes, and they should not be neglected.
Weibull distribution coefficients and theoretical AEP calculations for the years measured. All directions are included. SD stands for standard deviation.
To investigate directionally the effects of the neighboring farms on the
annual wind speed distributions, we fitted the Weibull distributions
sector-wise, and the results are shown in Fig. . The
sector 200–220∘ shows the influence of BR1 leading to a high
decrease in wind speeds. The distribution has not changed significantly in
2019 compared to the previous 2 years. This shows that the operation of BR2,
directly upstream of BR1, does not contribute to further wind speed reductions
in the microclimate of AV. This could be a result of the sparser layout of
the farm but cannot be verified without more relevant measurements or
simulations. In the sector 220–240∘, we see similar trends with a less
pronounced reduction. In 2019, the wind speeds are slightly reduced further,
which can be attributed to the small part of MRK operating in this sector. In the
sector 260–320∘, BR1 and BR2 do not affect the wind distributions as they
match with the earlier period. In the sector 240–260∘, which is influenced by
the part of BR1 that is located at distances of 3.5–8 km from FINO1, we
see that the wind speeds are not affected by looking at the years 2011–2017. TB1
seems not to influence the wind speeds as sector 260–320∘ shows no
significant deviations in the years 2011–2017. The influence of MRK, the
closest farm, is seen in the sector 240–320∘ with significant
reductions in the year 2019.
Fitted wind speed Weibull distributions per year and azimuth direction.
Next, we investigate how the TI experienced by FINO1 is influenced by the wake
interactions. In Fig. we show the mean TI per wind speed
bin for the different years and azimuth directions. In the sector
200–220∘, the increase in TI due to the presence of BR1 is
observed. At wind speeds of 6–14 ms-1, an increase of 40 %
to 60 % is seen. These results are constant in all years for both
periods suggesting that what we see is indeed attributed to wind farm
wakes. The operation of BR2 in 2019 directly upstream of BR1 does not seem to
lead to a further increase in TI at FINO1.
The sector 220–240∘ shows similar trends and levels of increase
in TI, with the effect of MRK in 2019 being visible as a further
increase. Looking at the sector 240–260∘ for the years 2016
and 2017, the increase in TI due to BR1 reaches a level of
20 %–35 %. This is smaller than the previous sector as a
smaller part of the upstream farm at a greater distance influences this
sector. In 2019, a significant increase of 70 %–120 % in TI is observed at wind speeds of
4–12 ms-1 compared
to the period 2011–2014. In the sector 260–320∘ for the
period 2016–2017 we see that the effect of TB1 is small with an increase of
2 %–5 % in TI. In the same sector looking at 2019, we
observe a significant increase in TI due to the MRK farm. The TI is increased
by 50 % to 100 % at wind speeds below
12 ms-1. This shows that the proximity of MRK to AV (distances of
1.4–7 km) has a very high impact on the wake-induced TI.
In all cases, the wake-induced turbulence is wind-speed-dependent. The TI
increase is higher at lower wind speeds since the upstream turbines are
operating with a higher thrust coefficient (below-rated operation), and the
wakes dissipate faster at higher wind speeds
.
Measured turbulence intensity per wind speed for the different directions and years. Solid lines and markers represent the mean values. The band represents the 15th and 85th percentiles.
Turbulence intensity mean values per direction for the different
years. Panel (a) shows data for the 6–8 ms-1 wind speed bin and (b) for the 13–15 ms-1 bin. The sectors defined by the dashed line indicate the azimuth sectors influenced by the denoted wind farms. The numbers in the legend indicate the amount of measurements used for each year.
Turbulence intensity probability of occurrence for different directions and years for wind speeds of 6–8 ms-1.
In Fig. we show the mean TI for all azimuth directions
(binned in 10∘ intervals) for two wind speed bins, 6–8 and
13–15 ms-1, representing the below- and above-rated operating
regions. The sector 30–170∘ is affected by the AV wakes and
cannot be used to derive meaningful conclusions but shows the consistency of
the measurements over the different periods, confirming that what we see in
the other sectors is attributed to inter-farm wakes. The peak observed around
270∘ in all measurements is attributed to the shadow of the
blizzard cage structure (see also ). This also
explains the more increased TI levels for all years in the sector
260–280∘ (seen also in Fig. ).
In the lower wind speed bin, we notice the directional influence of the wind
farms, with the effect of MRK in 2019 being the most dominant. Its influence is
observed already in the 220∘ direction. At 240∘ we
notice a drop in which the 2019 values match the 2016–2017 values, which could be
explained by the sparse placing of the turbines of MRK in this direction. In
the higher wind speed bin, we see a reduced effect on TI in all
directions. The observed level of increase in TI is almost the same in the
years 2017–2019. This suggests that even at close distances, as in the case
of MRK, the wake effects on TI at higher wind speeds are almost negligible.
Comparing the two wind speed bins, the wake effects on TI are much stronger at
lower wind speeds as explained earlier. Focusing on the years 2016–2019 we
notice that the wake-induced TI from MRK is more sensitive to the wind speed
compared to the one from BR1. This could be attributed to the smaller distance
of MRK from FINO1, in which the near-wake region is more sensitive to changes in
the thrust. Additionally, the larger size of the farm and the turbines in MRK
could be a factor contributing to this observation as the larger overall
thrust variations between wind speeds can lead to larger changes in the wake-induced turbulence. More research is needed on the topic, both numerically and
with measurements, to identify the exact mechanisms for this observation.
In order to examine how the wake-induced TI is distributed, we looked at the
sector-wise probability of occurrence of the TI bins for a wind speed of
6–8 ms-1 (Fig. ). In most cases, the probability
distributions in the years 2016–2019 are shifted towards higher TI levels
while maintaining the distribution shape. This indicates that the overall
levels of TI are increased, and the results shown previously are not
statistical artifacts from using the mean value.
For the sector 215–235∘, we see an increase in 2019 which can be attributed
to the small part at the edge of the MRK farm influencing FINO1. In the
235–245∘ sector, no increase occurred in 2019 to the mean value or the
probability distribution again due to the MRK layout. In sectors influenced by
the MRK farm (sector 245–325∘), the distributions are flatter
with a less sharp peak. This is more apparent, for instance, in the 250 and 300∘ directions. The reason for that is not clear. A preliminary
look at some of the 2020 data showed similar patterns. Thus, we believe that it
is not connected with some measurement issue. A correlation with the distance
and size of the MRK farm could be possible, but more research is required.
Fitted shear power law exponent at FINO1. (a) Mean wind shear power law exponent versus wind speed, considering all levels of turbulence intensity. (b) Mean wind shear power law exponent versus turbulence intensity including all wind speeds. The band represents the 15th and 85th percentiles. The numbers in the legend indicate the amount of measurements in the parentheses and the mean coefficient of determination for the power law fitting for each year.
Probability of occurrence for temperature difference between 50 ma.s.l. and sea surface for the different years. The numbers in the legend indicate the amount of measurements used for each year.
The effect of the inter-farm interactions on the shear power law exponent
(α) was investigated. By examining the shear per direction and wind
speed we could not identify consistent patterns. We found a stronger
correlation between α and TI. This can be seen in Fig.
where the fitted α is plotted against wind speed and TI for the azimuth
sector of 200–320∘. The mean shear exponent in the presence of
farm wakes (years 2016–2019) is lower for all wind speeds up to
16 ms-1. Looking at the band, we observe that the lower limit is
constant over all periods, while the upper is higher in the years 2011–2014
when AV was the only operating farm. This is directly caused by TI, which in
the earlier period was lower. We verified this by filtering with TI; when only
values above 5 % were kept, the two curves and the bands matched (not
shown here). The plot against TI, which includes all wind speeds, shows that
the mean value and the band limits are very close for all periods for TI
values higher than 4 %. For lower TI levels, α is found lower
for the later periods. Only a few measurements were available in this TI
region as events with such low turbulence are rare in the period 2015–2019 due
to the wake effects, and the fitting quality was not good as explained in the
next paragraph. To conclude, the perceived wind shear as expressed by α
was found to be decreased in the presence of farm wakes due to the increased
turbulence without clear correlations to distances and directions.
(a) mean significant wave height per wind speed for the different years considering all directions. (b) mean peak period over mean significant wave height for the different years. In both plots the linear fit is shown along with the relevant coefficient of determination.
The use of the power law exponent to describe vertical wind shear is common
practice in wind energy. Nevertheless, especially in wake situations, this is
difficult as the shear is not only driven by the stratification of the
atmosphere (i.e., by the temperature and pressure gradient) but also by the
mechanical mixing introduced in the boundary layer due to the wakes. Then, the
vertical wind profile cannot be adequately described by the power law making
the exponent fitting procedure quite uncertain. This was the case also in our
study, in which – especially in directions affected by the wake of MRK which is
very close to FINO1 – the best fit did not match well the observed
shape. Indicatively, the mean value of the coefficient of determination
(R2) regarding the quality of fitness between the measured heights and the
fitted α, considering all directions, wind speeds, and TI levels, was
on average 0.698 for the years 2011–2014, while for the later period it was
0.659. The standard deviation of R2 was close to 0.3 for all periods. The
characterization and modeling of the vertical wind profile downstream of wind
farms and individual turbines is a topic that still requires further research
that would extend the scope of the present study.
To examine whether the local climate has changed over the years, we looked at
the temperature and pressure measurements. The time series of temperature at
different heights and pressure at 20 ma.s.l. are shown in the
Appendix in Fig. . The trends are very similar for all the
years, indicating that the local climate has not changed. This suggests that
the changes we observed in the wind conditions are attributed to inter-farm
interactions. In Fig. we show the probability of occurrence of
the temperature difference between the water surface and the measurement at
50 m for the different years. This is used as an indicator of the
temperature gradient distribution that drives atmospheric stratification. The
distribution has not changed noticeably over the years. Only one year (2011)
shows lower-than-average values.
The oceanic measurements of Hs and Tp were also analyzed to examine
whether the conditions have changed over the years and whether the inter-farm
wakes have any effect on these values. In Fig. we show the
site's wave characteristics in terms of Hs per wind speed and Tp per
Hs along with their linear fits. No correlation was found between the
direction of wind and waves and Hs and Tp over the years. The measured
time series are shown in the Appendix in Fig. . Moreover, we
could not identify any influence of the inter-farm interactions on these values
in terms of magnitude, period, or frequency of occurrence.
Mean value of damage equivalent loads at the tower bottom, in the fore–aft direction, against wind speeds for the different directions and years. The band represents the 15th and 85th percentiles. The numbers in the legend indicate the amount of measurements used for each year.
Turbine response
After observing the changes in the metocean conditions of AV, we look at how
these affect the turbine's response. In Fig. we present the
tower bottom DEL in the fore–aft direction for the AV4 turbine for the
different years and azimuth directions. As the data from 2019 are not
included, the only relevant farms are TB1 and BR1. As discussed in Sect. 4.1,
the wakes of TB1 do not influence significantly the conditions at AV, which is
also seen in the loads. Hence, in the sector 260–320∘, the load
response is not changing, which also validates the consistency of the
measurements.
In the rest of the sectors, the loads are increased in the below-rated and the
transition regions, while at higher wind speeds the load level is similar (the
rated speed of the machine is about 13 ms-1). In the sector
200–220∘, we observe the larger differences at wind speeds of
7–11 ms-1 with an increase of 5 %–40 %. At
very low wind speeds, close to cut-in (about 4 ms-1 for this
machine), the loads are driven by the controller's behavior; hence we do not
see large deviations in the loads. At the transition region, at speeds of
11–14 ms-1, we observe a load increase at a level of
10 %–20 %. This effect is reduced with the increase in the
wind speed, and for wind speeds of about 16 ms-1 and higher the load
levels converge. Similar behavior is seen in the sector 220–240∘
with similar load increases as AV is still in the full wake of BR1. In the
sector 240–260∘, which is less influenced by BR1, we see a
reduced effect in loads. The highest increases of up to 30 % are found
at wind speeds of 8–11 ms-1. At higher wind speeds, above
16 ms-1, the loads are not affected.
In Fig. we present the mean DEL per direction for a wind
speed bin of 6–8 ms-1. As discussed for the similar plot for the
TI (see Fig. ), the sector 30–170∘ is
influenced by the wakes of AV. Hence, this sector can only be used to evaluate
the consistency of the load measurements over the years as the wind speed
perceived by the turbine is expected to be higher than the measured value by
FINO1. The influence of BR1 is seen in the 200–250∘ direction, with
the mean value, along with the percentiles, being shifted towards higher
values for the years 2016 and 2017. Furthermore, the effect of the TB1 farm is
minimal, suggesting that at this distance and for this specific farm size and
layout the farm wakes do not affect the tower's structural loading.
Mean value of damage equivalent loads at the tower bottom, for the fore–aft direction, per wind direction for the different years. Wind speed is 6–8 ms-1. The band represents the 15th and 85th percentiles. The numbers in the legend indicate the amount of measurements used for each year. The sectors defined by the dashed line indicate the azimuth sectors influenced by the denoted wind farms.
The SCADA data were also examined to identify possible correlations between the
farm effects and the turbine's response. The power production was not found to
be significantly affected by the increased TI. As a measure of the impact on
the pitch system, we evaluated the standard deviation of the blade pitch angle
from the SCADA data. In Fig. we show these values per direction
and wind speed. This signal is highly affected by minor changes to the
controller's behavior and the condition of the blades and pitch
system. Nevertheless, a clear trend of increase is observed when the two
periods (2011–2014 and 2015–2019) are compared. In the sector
200–260∘, the effect of BR1 is seen with an increase up to
levels of 30 %–40 %. In the sector
260–300∘, we see an increase in the pitch activity due to
TB1. This shows that, although the tower loads do not seem to be affected by
the small increase in TI in this sector, the pitch system is being more
loaded.
Mean standard deviation of the blade pitch angle per wind speed for different directions and years. The band represents the 15th and 85th percentiles. The numbers in the legend indicate the amount of measurements used for each year.
Mean standard deviation of the generator speed per wind speed for different directions and years. The band represents the 15th and 85th percentiles. The numbers in the legend indicate the amount of measurements used for each year.
We show the mean standard deviation of the generator speed signal over wind
speeds in Fig. . Similar to the pitch signal, this signal is also
influenced by factors, such as the controller settings and the condition of the
drive train and generator systems, leading to increased uncertainty and scatter
of the measurements. Still, as in the previous case, we observe increased
fluctuations of the generator speed due to the increased TI levels which could
lead to increased wear of the drive train. In sector 200–260∘,
we see an increase in the generator speed variation of up to 40 % in
below-rated operation as at higher wind speeds the regulation is handed to
the pitch controller. To a smaller extent, although still visible, the effect
of TB1 is seen in the 260–300∘ direction.
Discussion
Having a fully equipped offshore measuring platform located near a farm and
capturing data for long periods while the surroundings are changing is rare
and very beneficial for assessing offshore wind farm wake
effects. Nevertheless, still a lot of uncertainties come into play, as
explained in Sect. . The directional results are subject
to uncertainty as the sectors of influence of the neighboring farms are not
strict. This is due to possible errors in the exact locations of the turbines
and the measurement equipment, but it is more significantly influenced by the wake
expansion itself and the possible meandering of the farm wake. Moreover, this
could also be influenced by a possible yaw misalignment of the upstream
turbines. Additionally, as offshore farms are becoming larger, reaching several
kilometers, and turbine wakes are not linear features, a single point of
measurement is probably not enough to fully characterize the inflow that could
be varying across the wind farm.
Another important aspect relevant to this study is the consideration of
atmospheric stability classification. It is well established by research that
the wake effects (both at turbine and farm level) are directly correlated with
stability. In this study, we decided not to add this dimension to the data
processing as we focused on the analysis of the results cumulatively per
year. The temperature difference results shown are an indicator that the
annual stability distributions are similar over the different years. Moreover,
the boundary layer is more affected by the mixing taking place through the
interaction with the upstream farms, making it difficult to characterize
atmospheric stability based on turbulence intensity or vertical shear. Common
stability measures like the Monin–Obukhov length based on similarity theory
might not be applicable due to the wake (not ideal free-stream)
conditions. The chosen approach allows us to examine the cumulative effect of
the inter-farm interactions on the mean annual conditions experienced by the
turbines. This is of practical interest when assessing or simulating the
conditions and performance of a turbine at the site.
The findings here show the dependency of the perceived wake effects on the
distance and size of the farms. We summarize here some of the points we
observed that need more investigation. The operation of the farm BR2, located
directly upwind of BR1, did not seem to affect further the measurements at
FINO1 both in terms of wind speed and TI. Furthermore, the wake-induced
turbulence from the closest farm, MRK, was found to be more sensitive to wind
speeds. This could be correlated to the distance and also to the fact that the
machines are larger than the other farms examined and operate with a larger
variation in the thrust coefficient. Additionally, the characterization and
modeling of the vertical wind shear, especially when the distances between
the farms are smaller, have to be evaluated. In general, more research is needed to understand the correlation between wind farm size and layout, turbine capacity and size, and their effect on the strength of the wakes, the distance they can propagate, and their influence on the local microclimate of the downstream farm.
Another factor to consider is the weighting, in terms of probability of
occurrence, of wind speeds and directions when assessing the overall effect of
the inter-farm interactions. As seen from the wind rose and the Weibull
distributions, the wind speed and directional bins where the wake effects are
stronger are the most probable ones. This means that the impact they have on
lifetime loading and revenue is expected to be more significant when
calculating the aggregated values. This holds true for the specific site used
in this study but shows that in general considering this weighting is
important in the cases when inter-farm wakes are accounted for in
decision-making for processes like initial siting, maintenance planning,
end-of-life strategies, and operational and bidding strategies.
To facilitate further research on this topic, it could be very useful to have
measurements from the inflow of both the upstream and downstream farms,
ideally in a distributed manner. Moreover, at least some information such as
operational status or power production from both interacting farms (upstream
and downstream) would be very useful. Another aspect that could be worth
investigating in future scenarios, with higher wind farm density and even
closer spacing in favorable sites, is the coordinated operation of wind farm
clusters. It may be that, by adjusting sector-wise the operation of the
upstream farms according to their size, layout, and relative orientation, one
could increase the overall power production and even reduce the overall
structural loading.
Conclusions
In the present work we used metocean and turbine data to evaluate the effects
of the inter-farm interactions at the offshore site Alpha Ventus. The nearby
measurement platform FINO1 allowed us to examine how the local conditions have
changed from the period when AV was the only farm in the region to the later
period when four larger wind farms started operating at distances varying
from 1.4 to 9 km. The data were analyzed on an annual basis and with
regards to directions and wind speeds.
Systematic changes in the flow conditions and the turbine response were found
that can be attributed to wind farm wakes. This was validated by the agreement
of trends and magnitudes in the values examined for the two distinct periods,
as well as the directional results. Moreover, the general atmospheric conditions
were found not to be changed over the years in terms of temperature or
pressure.
The wind speeds were found to be reduced, with the shape parameter of the
fitted Weibull distributions being reduced by 15 % on average. For a
theoretical 5 MW wind turbine this translates to
15 %–20 % reductions in AEP. The increase in measured
turbulence intensity was found to be highly correlated with the distances
between the farms, reaching a maximum mean increase of 120 % for the closest
farms (1.5 km) in below-rated conditions. The vertical wind shear as
expressed by the fitted power law exponent was found to be reduced, although,
especially in the sectors related to the closest farms, it seemed to deviate
from the power law approximation. The oceanographic measurements suggested no
correlation between the presence of farm wakes and the values of significant
wave height and wave period.
Turbine measurements were used to investigate how the changed flow conditions
are experienced by the turbine of Alpha Ventus closest to FINO1. The tower
bottom fore–aft loads were influenced by the closer wind farm reaching an
increased level of 30 %–40 % for below-rated wind
speeds. Both generator speed variation and blade pitch activity were found to
be increased as well, even in sectors where loads did not change. This
suggests that inter-farm wake effects have to be considered for all the
systems and components of the turbine and not only for the structural loads.
The measurement results presented here show the possible technical and
financial impacts of inter-farm wakes which have to be considered when
planning the siting of wind farm clusters. Moreover, it shows the need for
analytical and simulation models that will be able to reproduce these effects and take into account the layout, sizing, and relative distances of neighboring farms.
Temperature and pressure time series
Temperature measurements at sea surface, at 30 m, and at 50 m and pressure measurements at 20 m over time for the different years.
In Fig. we show the time series of water surface temperature,
temperatures at 30 and 50 ma.s.l., and pressure at
20 ma.s.l. for the different years. A moving average of 2 d
is used for the results to make the seasonal trends clearer.
Oceanic data time series
Peak period and significant wave height over time for the different years.
In Fig. we show the time series of the measured wave
significant height and wave peak period for the different years. A moving
average of 2 d is used for the results to make the seasonal
trends clearer.
Code availability
The in-house codes used for processing the data are not publicly available but can be requested by direct communication with the authors.
Data availability
Data cannot be shared publicly as they are covered by a user agreement with the RAVE consortium for Alpha Ventus. Data can be requested directly from the RAVE consortium and the BSH at https://www.bsh.de/EN/DATA/data_node.html.
Author contributions
VP and MK conceived the idea of the research. VP performed the data processing and visualization and wrote the manuscript. MK and VP developed the codes for data processing and visualization. VP, MK, AC, and PWC revised the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work is funded by the German Federal Ministry for Economic Affairs and
Energy (BMWi) in the framework of the national joint research project RAVE –
OWP Control (ref. 0324131B) and is part of the research done in the WindForS
research cluster. We would also like to thank Senvion and DNV for providing
the turbine measurements. Additional thanks go to the Federal
Maritime and Hydrographic Agency (BSH) for providing access to the measurement
database. Finally, we thank 4C Offshore for allowing the use of their maps.
Financial support
This research has been supported by the Bundesministerium für Wirtschaft und Energie (grant no. RAVE – OWP Control 0324131B).This open-access publication was funded by the University of Stuttgart.
Review statement
This paper was edited by Joachim Peinke and reviewed by Nicolai Gayle Nygaard and one anonymous referee.
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