WindResourceAndSiteAssessment.pdf

CHAPTER 2
Wind resource and site assessment
Wiebke Langreder
Wind & Site, Suzlon Energy, Århus, Denmark.
Wind farm projects require intensive work prior to the finalizing of a project.
The wind resource is one of the most important factors for the financial viability of
a wind farm project. Wind maps representing the best estimate of the wind resource
across a large area have been produced for a wide range of scales, from global
down to local government regions. They do not substitute for wind measurements –
rather they serve to focus investigations and indicate where on-site measurements
would be merited. This chapter explains how wind resource can be assessed. The
steps in this process are explained in detail, starting with initial site identifica-
tion. A range of aspects concerning wind speed measurements is then covered
including the choice of sensors, explaining the importance of proper mounting and
calibration, long-term corrections, and data analysis. The difficulty of extrapolat-
ing the measured wind speed vertically and horizontally is demonstrated, leading
to the need for flow models and their proper use. Basic rules for developing a
layout are explained. Having analysed wind data and prepared a layout, the next
step is energy yield calculation.The chapter ends by exploring various aspects of
site suitability.
1 Initial site identification
The wind resource is one of the most critical aspects to be assessed when plan-
ning a wind farm. Different approaches on how to obtain information on the wind
climate are possible. In most countries where wind energy is used extensively,
some form of general information about the wind is available. This information
could consist of wind maps showing colour coded wind speed or energy at a spe-
cific height. These are often based on meso-scale models and in the ideal case, are
validated with ground-based stations. The quality of these maps varies widely and
depends on the amount and precision of information that the model has been fed
with, the validation process and the resolution of the model.
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50 Wind Power Generation and Wind Turbine Design
Wind atlases are normally produced with Wind Atlas Analysis and Application
Program (WAsP, a micro-scale model, see Section 4.3) or combined models which
involve the use of both meso- and micro-scale models, and are presented as a col-
lection of wind statistics. The usefulness of these wind statistics depends very
much on the distance between the target site and the stations, the input data they
are based on, the site as well as on the complexity of the area, both regarding
roughness and orography. Typically the main source of information for wind
atlases is meteorological stations with measurements performed at a height of
10 m. Meso-scale models additionally use re-analysis data (see Section 3.1.1).
Care has to be taken since the main purpose of these data is to deliver a basis for
general weather models, which have a much smaller need for high precision wind
measurements than wind energy. Thus the quality of wind atlases is not sufficient
to replace on-site measurements [1].
Nature itself frequently gives reasonable indications of wind resources. Particu-
larly flagged trees and bushes can indicate a promising wind climate and can give
valuable information on the prevailing wind direction.
A very good source of information for a first estimate of the wind regime is
production data from nearby wind farms, if available.
No other step in the process of wind farm development has such significance to
the financial success as the correct assessment of the wind regime at the future
turbine location. Because of the cubic relationship between wind speed and energy
content in the wind, the prediction of energy output is extremely sensitive to the
wind speed and requires every possible attention.
2 Wind speed measurements
2.1 Introduction
The measured wind climate is the main input for the flow models, by which you
extrapolate the spot measurement vertically and horizontally to evaluate the energy
distribution across the site. Such a resource map is the basis for an optimised lay-
out. The number and height of the measurement masts should be adjusted to the
complexity of the terrain as with increasing complexity, the capability of flow
models to correctly predict the spatial variation of the wind decreases. The more
complex the site, the more and the higher masts have to be installed to ensure a
reasonable prediction of the wind resource.
Unfortunately wind measurements are frequently neglected. Very often the
measurement height is insufficient for the complexity of the site, the number
of masts is insufficient for the size of the site, the measurement period is too
short, the instruments are not calibrated, the mounting is sub-standard or the
mast is not maintained. It cannot be stressed enough that the most expensive
part when measuring wind is the loss of data. Any wind resource assessment
requires a minimum measurement period of one complete year in order to
avoid seasonal biases. If instrumentation fails due to lightning strike, icing,
vandalism or other reasons and the failure is not spotted rapidly, the lost data

Wind Resource and Site Assessment 51
will falsify the results and as a consequence the measurement period has to
start all over again. Otherwise the increased uncertainty might jeopardise the
feasibility of the whole project.
2.2 Instruments
2.2.1 General
Wind speed measurements put a very high demand on the instrumentation because
the energy density is proportional to the cube of the mean wind speed. Further-
more, the instruments used must be robust and reliably accumulate data over
extended periods of unattended operation. The power consumption should be low
so that they can operate off the grid.
Most on-site wind measurements are carried out using the traditional cup ane-
mometer. The behaviour of these instruments is fairly well understood and the
sources of error are well known. In general, the sources of error in anemometry
include the effects of the tower, boom and other mounting arrangements, the ane-
mometer design and its response to turbulent and non-horizontal flow characteris-
tics, and the calibration procedure. Evidently, proper maintenance of the
anemometer is also important. In some cases, problems arise due to icing of the
sensor, or corrosion of the anemometer at sites close to the sea. The current version
of the internationally used standard for power curve measurements, the IEC stan-
dard 61400-12-1 [2], only permits the use of cup anemometry for power curve
measurements. The same requirements for accuracy are valid for wind resource
measurements. Therefore it is advisable to use also these instruments for wind
resource assessment.
Solid state wind sensors (e.g. sonics) have until recently not been used exten-
sively for wind energy purposes, mainly because of their high cost and a higher
power consumption. These have a number of advantages over mechanical ane-
mometers and can further provide measurements of turbulence, air temperature,
and atmospheric stability. However, they also introduce new sources of error which
are less known, and the overall accuracy of sonic anemometry is lower than for
high-quality cup anemometry [3].
Recently, remote sensing devices based either on sound (Sodar) or on laser
(Lidar) have made an entry into the market. Their clear merit is that they
replace a mast which can have practical advantages. However, they often
require more substantial power supplies which bring other reliability and
deployment issues. Also more intensive maintenance is required since the
mean time between failures does not allow unattended measurements for peri-
ods required for wind resource assessment. While the precision of a Lidar
seems to be superior to the Sodar, and often comparable to cup anemometry
[4], both instruments suffer at the moment from short-comings in complex ter-
rain due to the fact that the wind speed sampling takes place over a volume,
and not at a point.
Remote sensing technologies are currently evolving very rapidly and it is
expected they will have a significant role to play in the future.

2.2.2 Cup anemometer
The cup anemometer is a drag device and consists typically of three cups each
mounted on one end of a horizontal arm, which in turn are mounted at equal angles
to each other on a vertical shaft. A cup anemometer turns in the wind because the
drag coefficient of the open face cup is greater than the drag coefficient of the
smooth surface of the back. The air flow past the cups in any horizontal direction
turns the cups in a manner that is proportional to the wind speed. Therefore, count-
ing the turns of the cups over a set time period produces the average wind speed
for a wide range of speeds.
Despite the simple geometry of an anemometer its measurement behaviour
depends on a number of different factors. One of the most dominant factors is the
so-called angular response, which describes what components of the wind vector are
measured [3]. A so-called vector anemometer measures all three components of the
wind vector, the longitudinal, lateral and vertical component. Thus this type of ane-
mometer measures independently of the inflow angle and is less sensitive to mount-
ing errors, terrain inclination and/or thermal effects. However, for power curve
measurements the instrument must have a cosine response thus measuring only the
horizontal component of the wind [2]. Since for energy yield calculations the mea-
surement behaviour of the anemometer used for the power curve and used for
resource assessment should be as similar as possible, it is advisable to also use an
anemometer with a cosine response for resource assessment. One of the key argu-
ments for using such an instrument for power curve measurements is that the wind
turbine utilises only the horizontal component. This is, however, a very simplified
approach as, particularly for large rotors, three-dimensional effects along the blades
leads to a utilisation of energy from the vertical component. Care has to be taken
when using a cosine response anemometer as it is sensitive to mounting errors.
One of the most relevant dynamic response specifications is the so-called over-
speeding. Due mainly to the aerodynamic characteristics of the cups, the anemom-
eter tends to accelerate faster than it decelerates, leading to an over-estimate of
wind speed particularly in the middle wind speed range.
Another dynamic response specification is the response length or distance con-
stant, which is related to the inertia of the cup anemometer. The dynamic response
can be described as a first order equation. When a step change of wind speed from
U to U + Δu hits the anemometer it will react with some delay of exponential
shape. The distance constant, i.e. the column of air corresponding to 63% recovery
time for a step change in wind speed, should preferably be a few meters or less.
Different methods to determine the response length are described in [3].
2.2.3 Ultrasonic anemometer
Ultrasonic or sonic anemometers use ultrasonic waves for measuring wind speed
and, depending on the geometry, the wind direction. They measure the wind speed
based on the time of flight between pairs of transducers. Depending on the num-
ber of pairs of transducers, either one-, two- or three-dimensional flow can be
measured. The travelling time forth and back between the transducers is different
because in one direction the wind speed component along the path is added to the

sound speed and subtracted from the other direction. If the distance of the trans-
ducers is given with s and the velocity of sound with c then the travelling times
can be expressed as
1 2
and
s s
t t
c u c u
= =
+ −
(1)
These equations can be re-arranged to eliminate c and to express the wind speed
u as a function of t1, t2 and s. The sole dependency on the path length is advanta-
geous, as the speed of sound depends on air density and humidity:
1 2
1 1
2
s
u
t t
⎛ ⎞
= −
⎜ ⎟
⎝ ⎠
(2)
It can be seen that once u is known, c can be calculated and from c the tempera-
ture can be inferred (slightly contaminated with humidity, this is known as the
“sound virtual temperature”). The spatial resolution is determined by the path
length between the transducers, which is typically 10–20 cm. Due to the very fine
temporal resolution of 20 Hz or better the sonic anemometer is very well suited for
measurements of turbulence with much better temporal and spatial resolution than
cup anemometry.
The measurement of different components of the wind, the lack of moving parts,
and the high temporal resolutions make the ultrasonic anemometer a very attrac-
tive wind speed measurement device. The major concern, inherent in sonic ane-
mometry, is the fact that the probe head itself distorts the flow – the effect of which
can only be evaluated in detail by a comprehensive wind tunnel investigation. The
transducer shadow effect is a particularly simple case of flow distortion and a well-
known source of error in sonics with horizontal sound paths. Less well known are
the errors associated with inaccuracies in probe head geometry and the tempera-
ture sensitivity of the sound transducers. The measurement is very sensitive to
small variations in the geometry, either due to temperature variations and/or
mechanical vibrations due to wind. Finally, specific details in the design of a given
probe head may give rise to wind speed-dependent errors.
2.2.4 Propeller anemometer
A propeller anemometer typically has four helicoid-shaped blades. This propeller
can either be mounted in conjunction with a wind vane or in a fixed two- or three-
dimensional arrangement (Fig. 1). While a cup anemometer responds to the dif-
ferential drag force, both drag and lift forces act to turn the propeller anemometer.
Similar to a cup anemometer the response of the propeller anemometer to slow
speed variations is linear above the starting threshold.
Propeller anemometers have an angular response that deviates from cosine. In fact
the wind speed measured is somewhat less than the horizontal component [5]. If a
propeller is used in conjunction with a vane the propeller is in theory on average
oriented into the wind and thus the angular response is not so relevant. However,
the vane often shows an over-critical damping which leads to misalignment and

thus to an under-estimate of the wind speed. If propellers are mounted in a fixed
arrangement the under-estimate of the wind speed is even more significant as the
axis of the propeller is not aligned with the wind direction.
2.2.5 Remote sensing
An alternative to mast-mounted anemometry are ground-based remote sensing sys-
tems. Two systems have found some acceptance in the wind energy community:
Sodar and Lidar. Both the Sodar (SOund Detection And Ranging) and the Lidar
(LIght Detection And Ranging) use remote sensing techniques based respectively
on sound and light emission, in combination with the Doppler effect. The signal
emitted by the Sodar is scattered by temperature fluctuations while the signal emit-
ted by a Lidar is scattered by aerosols. In contrast to the very small measurement
volume of a cup anemometer, both remote sensing devices measure large volumes,
which change with height. Both types require significantly more power than a cup
anemometer making the use of a generator necessary (for the majority of models)
if no grid is available.
2.2.5.1 Sodar
Different types of Sodars are available with different arrangements of the loud-
speaker and receiver. Most commonly the sound pulse generated by a loudspeaker
array can be tilted by electronically steering the array to different directions (phased
array Sodar). The combination of three beams, one in the vertical direction and
Figure 1: Propeller anemometer in fixed three-dimensional arrangement.

two others, tilted to the vertical and perpendicular to each other’s planes produce
the three-dimensional velocity field (Fig. 2).
One of the drawbacks of a Sodar is its dependency on temperature fluctuations
which in turn means that a Sodar is at a disadvantage under neutral atmospheric
conditions, which are related to high wind speed situations [8]. Another type of
Sodar, the bistatic, reacts on velocity inhomogeneities rather than temperature
fluctuations and does not exhibit this problem. However, there is no commercially
available bistatic Sodar and the principle requires separate, spatially separated
transmitter and receiver, which makes it less practical than conventional Sodar.
The signal-to-noise ratio of the Sodar is also known to deteriorate with height,
resulting in a reduced number of valid signal returns. Thus a measured profile is
difficult to interpret as the profile might be based on a different number of mea-
surements for each height.
At the same time, due to the tilt of the beams, the measurement takes place in three
non-overlapping volumes.Assuming a typical tilt angle of 17°, the distance between
the tilted volumes and the distance between the vertical and a tilted volume is pre-
sented in Table 1 for a number of altitudes. As a consequence, the interpretation of
a measured profile becomes difficult because the measurement volume changes
with height, especially in complex terrain.
Since the Sodar is an acoustic system, the presence of noise sources can influ-
ence the Sodar’s function. The source of noise could be rain but any other noise,
Figure 2: The principle of a three-beam phased array Sodar [7].

for example from animals, can have an adverse effect. A particularly critical issue
is the increased background noise due to high wind speeds [9]. False echoes from
the Sodar’s enclosure or nearby obstacles can also lead to a falsified signal. Other
parameters which may influence the Sodar measurement are errors in the vertical
alignment of the instrument, temperature changes at the antenna and, especially
for three-beam Sodars, changes in wind direction [8].
The measurement accuracy of Sodar systems cannot match that of cup ane-
mometry and unless high acoustical powers are used, their availability falls in
high wind speeds. A hybrid system comprising a moderately tall mast (say 40 m)
and a relatively low power (30–100 W electrical) Sodar has many attractive
features – the high absolute accuracy and high availability of the cup anemom-
eter complements the less accurate but highly relevant vertical resolution
obtained from the Sodar [10].
2.2.5.2. Lidar
Until recently, making wind measurements using Lidars was prohibitively
expensive and essentially limited to the aerospace and military domain. Most
limitations were swept aside by the emergence of coherent lasers at wave-
lengths compliant with fibre optic components (so-called ‘fibre lasers’). Since
light can be much more precisely focused and spreads in the atmosphere much
less than sound, Lidar systems have an inherently higher accuracy and better
signal to noise ratio than a Sodar. The Lidar works by focusing at a specific
distance and it measures the scattering from aerosols that takes place within
the focal volume.
The operation of the Lidar is influenced by atmospheric conditions (e.g. fog,
density of particles in the air). Lack of particles influences its response, some-
times prohibiting measurement while fog can severely attenuate the beam
before it reaches the measurement height. Rain also reduces the Lidar’s ability
to measure, as scattering from the falling droplets can result in errors in the
wind speed, particularly its vertical components. Other parameters that influ-
ence the measurement are, as in the case of the Sodar: errors in the vertical
alignment of the instrument and uncertainties in the focusing height. The Lidar,
Table 1: The distance between the three beams at a number of heights [9].
Altitude (m)
Distance (m)
Tilt–vertical Tilt–tilt
40 11.7 16.5
80 23.4 33.1
120 35.1 49.6
160 46.8 66.2
200 58.5 82.7

being an optical instrument, is also susceptible to influences from the presence
of dirt on the output window; hence there is a need for a robust cleaning device
of the Lidar window [9].
Two working principles of fibre-base Lidars are in use: one uses a continu-
ous-wave system with height discrimination achieved by varying focus. The
laser light is emitted through a constantly rotating prism giving a deflection of
30° from the vertical. This Lidar system scans a laser beam about a vertical
axis from the ground, intercepting the wind on a 360° circumference. By
adjusting the laser focus, winds may be sampled at a range of heights above
ground level.
The other system uses a pulsed signal with a fixed focus. It has a 30° prism to
deflect the beam from the vertical but here the prism does not rotate continuously.
Instead, the prism remains stationary whilst the Lidar sends a stream of pulses in
a given direction, recording the backscatter in a number of range gates (fixed time
delays) triggered by the end of each pulse [10] (see Fig. 3).
Unlike pulsed systems, continuous-wave Lidar systems do not inherently ‘know’
the height from which backscatter is being received. A sensibly uniform vertical
profile of aerosol concentration has to be assumed in which case the backscattered
energy is from the focused volume. The obtained radial wind speed distribution in
this case is dominated by the signal from the set focus distance. The assumption of
vertical aerosol homogeneity unfortunately fails completely in the fairly common
case of low level clouds (under 1500 m). Here, the relatively huge backscatter
from the cloud base can be detected even though the cloud is far above the focus
distance. The resulting Doppler spectrum has two peaks – one corresponding to
the radial speed at the focused height and a second corresponding to the (usually)
higher speed of the cloud base. Unless corrected for, this will introduce a bias to
the wind speed measurement. For this reason, the continuous-wave Lidar has a
cloud-correction algorithm that identifies the second peak and rejects it from the
Figure 3: Working principle of a pulsed Lidar.

spectrum. An extra second scan with a near-collimated output beam is inserted
into the height cycle. The spectra thus measured are used to remove the influence
of the clouds at the desired measuring heights [10].
The two systems have fundamental differences concerning the measurement
volume. The continuous-wave system adjusts the focus so winds may be sam-
pled at a range of heights above ground level. The backscattered signal comes
mainly from the region close to the beam focus, where the signal intensity is at
its maximum. While the width of the laser beam increases in proportion to focus
height, its probe length increases non-linearly (roughly the square of height;
Table 2). The vertical measuring depth of the pulsed system depends on the
pulse length and is constant with sensing range [10]. A continuous-wave system
can measure wind speed at heights from less than 10 m up to a maximum of
about 200 m. Pulsed Lidars are typically blinded during emission of the pulse
and this restricts their minimum range to about 40 m, with the maximum range
usually limited only by signal-to-noise considerations and hence dependent on
conditions.
Like the Sodar, both Lidar systems rely on the assumption of horizontally homo-
geneous flow. In complex terrain this assumption is violated, increasingly so as the
terrain complexity increases. There are indications that errors of 5–10% in the
mean speed are not uncommon [12–14]. Only a multiple Lidar system, in which
units are separated along a suitably long baseline, could eliminate this inherent
error as explained above.
In general, care has to be taken when performing short-term measurements with
remote sensing devices. The vertical profile varies significantly with different
atmospheric stabilities. Thus the measurement campaign using remote sensing
should, similarly to cup anemometry, be a minimum of 1 year.
Currently work is in progress for a Best Practice Guideline for the use of remote
sensing.
2.3 Calibration
As explained in Section 2.2.2, the turns of an anemometer are transformed into a
wind speed measurement by a linear function. The scale and offset of this trans-
fer function are determined by wind tunnel calibration of the anemometer. Strict
requirements concerning the wind tunnel test are specified in [2]. Please note that
Table 2: Beam half-length versus focal distance of a continuous-wave Lidar [11].
Altitude (m) Beam half-length, L (m)
40 2.5
60 6
100 16
200 65

such strict calibration procedures are only in place for cup anemometers. Highest
quality calibrations are ensured when calibrating in wind tunnels that have been
accredited by MEASNET. MEASNET members participate regularly in a round robin
test to guarantee interchangeability of the results, which has increased the quality of
the calibration significantly. It should be kept in mind that even calibrations according
to highest standard bear an uncertainty of around 1–2%.
Currently there are no calibration standards available for sonics and remote
sensing devices.
2.4 Mounting
Accurate wind speed measurements are only possible with appropriate mounting
of the cup anemometry on the meteorological mast. In particular, the anemometer
shall be located such that the flow distortion due to the mast and the side booms is
minimised. The least flow distortion is found by mounting the anemometer on top of
the mast at a sufficient distance to the structure. Other instruments, aviation lighting,
and the lightning protection should be mounted in such a way that interference with
the anemometer is avoided. Figure 4 shows a possible top mounting arrangement.
Boom-mounted anemometers are influenced by flow distortion of both the mast
and the boom. Flow distortion due to the mounting boom should be kept below
0.5% and flow distortion due to the mast should be kept below 1%. If the anemom-
eter is mounted on a tubular side boom, this can be achieved by mounting the
anemometer 15 times the boom diameter above the boom. The level of flow distor-
tion due the mast depends on the type of mast and the direction the anemometer is
facing with respect to the mast geometry and the main wind direction.
Figure 4: Example top mounted anemometers [2].

Figure 5 shows an example of flow distortion around a tubular mast. It can be seen
that there is a deceleration of the flow upwind to the mast, acceleration around it and
a wake behind it. The least disturbance can be seen to occur if facing the wind at 45°.
The flow distortion around a lattice mast is somewhat more complicated to
determine. Additionally to the orientation of the wind and the distance of the ane-
mometer to the centre of the mast it also depends on the solidity of the mast and
the drag. Figure 6 shows an example of flow distortion. Again a deceleration in
front of the mast can be observed while there is acceleration at the flanks. Mini-
mum distortion is achieved when the anemometer is placed at an angle of 60°.
More details on how to determine the flow distortion can be found in [3] or [2].
In general, mounting of the anemometer at the same height as the top of the
mast should be avoided since the flow distortion around the top of the mast is
highly complex and cannot be corrected for.
2.5 Measurement period and averaging time
The energy yield is typically calculated referring to the annual mean wind speed
of the site. Unfortunately the annual average wind speed varies significantly.
Depending on the local climate the annual averages of wind speed might vary
around ±15% from one year to the next. To reduce the uncertainty of the inter-
annual variability it is strongly recommended to perform a long-term correction
of the measured data (see Section 3.1). On a monthly scale, the variations of the
Figure 5: Iso-speed plot of local flow speed around a cylindrical mast, norma-
lised by free-field wind speed (from left); analysis by two-dimensional
Navier-Stokes computations [3].

wind speed are much more significant, reaching – depending on the local climate –
variations of ±50%. Thus it is of crucial importance to measure complete years
since the monthly variations are too large to be corrected and, as a consequence,
partial year data suffers from a seasonal bias.
Wind data is normally sampled for 10 min, the logger then calculates the aver-
ages and the standard deviations, the latter being necessary for determining the
turbulence intensity. The averaging period of 10 min allows the direct use of the
data for load calculations as they also refer to 10-min averages. Great care has to
be taken when the averaging period is shorter than 10 min as the standard devia-
tion and thus the turbulence intensity will be lower in comparison to 10-min
averaging period. This can lead to under-estimated turbulence-induced loads.
3 Data analysis
3.1 Long-term correction
3.1.1 Introduction
Variability is an intrinsic feature of climate. Precautions are necessary as the
weather changes from year to year and between consecutive decades. The data
which form the basis of any wind resource study cover a limited period of time,
which in many cases is less than 5 years. The question therefore arises: to what
extent is that period representative for the longer-term climate. A study of climatic
Figure 6: Iso-speed plot of local flow speed around a triangular lattice mast with a
CT of 0.5 normalised by free-field wind speed (from left); analysis by two-
dimensional Navier-Stokes computations and actuator disk theory [3].

variability in Northern Europe shows that variations in wind energy of up to 30%
can be expected from one decade to another (see Fig. 7). In another study [15] it
was found from an analysis of the expected power output for a 45 m high wind
turbine over a 22-year period that the inter-annual variation in power corresponds
to a mean relative standard deviation of approximately 13%. For the proper assess-
ment of the economics of wind power utilisation, such variability must obviously
be borne in mind [16].
Any short-term measurement should therefore be correlated with long-term
data covering ideally 30 years from a representative station. Unfortunately it is
very difficult in most cases to obtain reliable, consistent long-term data since
weather stations are frequently subject to changes in environment by, for exam-
ple, growing vegetation or building activity. Therefore – and particularly in
very remote areas – the use of re-analysis data might be a feasible alternative.
Most commonly used is NCEP/NCAR data from NCEP (National Centre for
Environmental Prediction) and NCAR (National Center for Atmospheric
Research) in the US. These institutes continuously perform a global re-analysis
of weather data. The objective is to produce homogeneous data sets covering a
decade or more of weather analysis with the same data assimilation systems.
This means that data from synoptic weather stations, radio sondes, pilot bal-
loons, aircraft, ships, buoys and satellites are collected, controlled, gridded, and
prepared for initialisation of a global numerical weather prediction model. By
running past-periods on a global scale, the data is re-analysed in a globally
consistent manner which enables it to be used in some situations for long-term
correlation purposes.
The advantages are that the period of available re-analysed data can be much
longer than the period of available on-site data, the data is neither subject to icing
Figure 7: Mean energy in wind for consecutive 5-year periods based on a time
series from Hesselø, Denmark, 1873–1982 [17].

nor seasonal influences from vegetation and finally there should be no long-term
equipment trends. However the quality of re-analysis data still depends on the
quality of the input data, with the result that in sparsely instrumented regions, the
re-analysis data can still suffer from deficiencies in individual instrumentation and
data coverage. The same care should be applied to the use of re-analysis data, as
with ground-based data. The NCEP/NCAR data is available in the form of pres-
sure and surface wind data in a 2.5° grid corresponding to a spacing of approxi-
mately 250 km. The data consists of values of wind speed and direction for four
instantaneous values per day (every 6 hours).
The statistical method for long-term correcting data is called Measure-
Correlate-Predict or MCP. This method is based on the assumption that the
short- and long-term data sets are correlated. This correlation can be established
in different ways depending on the data quality and the comparability of the two
wind climates.
3.1.2 Regression method
If one mast is correlated with a second on-site mast, a linear regression either
omnidirectional or by wind direction sectors (typically 30°) might be best suited.
For the concurrent period, the wind speeds are plotted versus each other and a
linear regression based on the least-square fit is established. This relationship is
used to extend the shorter data set with synthetic data based on the longer data
set. The regression coefficient is a measure of the quality of the correlation.
R2
should not be less than 70%. The same method might be appropriate for a
short-term measurement on-site and a reference station in some distance if the
orography and the wind roses are closely related. If the wind roses vary, care has
to be taken since the wind rose of the reference station will be transferred to the
site by applying a linear regression, which can have a significant impact on the
layout as well as the energy yield. Another inherent problem of this methodol-
ogy is the decreasing temporal correlation between the site and the reference
station with increasing distance. The introduction of averaging of the two data
sets can improve the correlation.
3.1.3 Energy index method
Rather than transposing the wind distribution from the reference station the energy
index method determines a correction factor for the short-term data. For the con-
current period, the energy level of the reference data set is determined and com-
pared with the long-term energy. The resulting ratio is then applied as correction to
the short-term on-site data set. The correlation is best proven comparing monthly
mean wind speeds of the two data sets.
This method has the main advantage that the on-site measured wind rose is not
altered. The energy index method is particularly suited for NCEP/NCAR data
since the low temporal resolution of NCEP/NCAR prohibits the use of the more
detailed regression method. However, care has to be taken since NCEP/NCAR
data represents only geostrophic wind and does not reflect local wind climates like
wind tunnel effects across a mountain pass or thermal effects.

3.2 Weibull distribution
It is very important for the wind industry to be able to relatively simply describe
the wind regime on site. Turbine designers need the information to optimise the
design of their turbines, so as to minimise generating costs. Turbine investors need
the information to estimate their income from electricity generation.
One way to condense the information of a measured time series is a histogram.
The wind speeds are sorted into wind speed bins. The bin width is typically 1 m/s.
The histogram provides information how often the wind is blowing for each wind
speed bin.
The histogram for a typical site can be presented using the Weibull distribution
expressing the frequency distribution of the wind speed in a compact form. The
two-parameter Weibull distribution is described mathematically as
−
⎛ ⎞
⎛ ⎞ ⎛ ⎞
= −
⎜ ⎟
⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠
⎝ ⎠
1
( ) exp
k k
k u u
f u
A A A (3)
where f(u) is the frequency of occurrence of wind speed u. The scaling factor A is
a measure for the wind speed while the shape factor k describes the shape of the
distribution. The cumulative Weibull distribution F(u) gives the probability of the
wind speed exceeding the value v and is given by the simple expression:
( ) exp
k
u
F u
A
⎛ ⎞
⎛ ⎞
= −
⎜ ⎟
⎜ ⎟
⎝ ⎠
⎝ ⎠
(4)
Following graph shows a group of Weibull distributions with a constant mean
wind speed of 8 m/s but varying k factor. Note that high wind speeds become more
probable with a low k factor.
The Weibull distribution can degenerate into two special distributions, namely
for k = 1 the exponential distribution and k = 2 the Rayleigh distribution. Since
observed wind data exhibits frequency distributions which are often well described
by a Rayleigh distribution, this one-parameter distribution is sometimes used by
wind turbine manufacturers for calculation of standard performance figures for
their machines. Inspection of the k parameter shows that, especially for Northern
European climates, the values for k are indeed close to 2.0.
On a global scale, the k factor varies significantly depending upon local climate
conditions, the landscape, and its surface (Fig. 8). A low k factor (<1.8) is typical
for wind climates with a high content of thermal winds. A high k factor (>2.5) is
representative for very constant wind climates, for example trade winds. Both
Weibull A and k parameters are dependent on the height and are increasing up to
100 m above ground (Fig. 9). Above 100 m the k parameter decreases.
The Weibull distribution is a probability density distribution. The median of the
distribution corresponds to the wind speed that cuts the area into half. This means
that half the time it will be blowing less than the median wind speed, the other half

it will be blowing faster. The mean wind speed is the average of the distribution.
The wind speed with the highest frequency is called the modal value.
Many different methods can be used for fitting the two Weibull parameters to a
histogram. Since in general the observed histograms will show deviations, a fitting
procedure must be selected which focuses on the wind speed range relevant to the
0
0.02
0.04
0.06
0.08
0.1
0.12
0 10 20 30
Frequency
[%]
Wind Speed [m/s]
1.25
1.5
1.75
2
2.25
Figure 8: Weibull distributions for constant mean wind speed (8 m/s) and varying
k factors.
Figure 9: Weibull A and k parameters as a function of height for a roughness class
2 [16].

application. Here the emphasis should be on the energy containing part of the
spectrum. Thus a moment fitting method is normally used with focus on medium
to high wind speeds but not extreme wind speeds. The requirements for determin-
ing the two Weibull parameters are that the total wind energy in the fitted Weibull
distribution and the observed distribution are equal. Additionally the frequencies
of occurrence of the wind speeds higher than the observed average wind speed
have to be the same for both distributions. The combination of these two require-
ments leads to an equation in k only, which can be solved by a standard root-
finding algorithm.
As a rough approximation, the relationship between the Weibull parameter A
and k and the mean wind speed can be described as follows:
1/
0.434
0.568
k
U A
k
⎛ ⎞
≈ +
⎜ ⎟
⎝ ⎠
(5)
4 Spatial extrapolation
4.1 Introduction
Analyses of wind speed measurements lead to a detailed description of the wind
climate at one point. In order to calculate the energy yield of the whole wind field,
the results from the measurements have to be extrapolated horizontally to cover
the wind farm area, and vertically if the measurements were not performed at hub
height. Normally for the spatial extrapolation, computer models are used which
are primed with wind speed data measured on site.
In the following section, parameters affecting the vertical extrapolation are
described. This is followed by a description of the concept of flow models, and
particularly WAsP as the most commonly used model in the industry.
4.2 Vertical extrapolation
4.2.1 Introduction
The planetary boundary layer (PBL), also known as the atmospheric boundary
layer (ABL), is the lowest part of the atmosphere and its behaviour is directly
influenced by its contact with the planetary surface. Above the PBL is the "free
atmosphere" where the wind is approximately geostrophic (parallel to the iso-
bars) while within the PBL the wind is affected by surface drag and turns across
the isobars. The free atmosphere is usually non-turbulent, or only intermittently
turbulent. The surface layer is the lowest part of the ABL. Its height is normally
taken as around 10% of the ABL height but varies significantly during seasons and
day times.
The change of wind speed with height is described by the vertical wind pro-
file, which is the key for extrapolation of the measured wind speed to hub height.
Generally the wind speed increases with increasing height above the ground.

The vertical wind profile in the surface layer can be described by a number of
simplified assumptions.
The profile depends, next to roughness and orography, on the vertical tem-
perature profile, which is also referred to as atmospheric stability. Three general
cases can be categorised (Fig. 10). In neutral conditions, the temperature profile
is adiabatic meaning that there is equilibrium between cooling/heating and
expansion/contraction with no vertical exchange of heat energy. The tempera-
ture decreases with around 1°C per 100 m in this situation. Neutral conditions
are typical for high wind speeds. The vertical wind profile depends only on
roughness and orography.
In unstable conditions, the temperature decreases with height faster than in the
neutral case. This is typically the case during summer time where the ground is
heated. As a result of the heating the air close to ground starts rising since the air
density in the higher layers is lower (convective conditions). As a consequence, a
vertical exchange of momentum is established leading to a higher level of turbu-
lence. The vertical wind shear is generally small in these situations due to the
heavy mixing.
In stable conditions, which are typical for winter or night time, the air close to
the ground is cooler than the layers above. The higher air density with increasing
height suppresses all vertical exchange of momentum. Thus turbulence is sup-
pressed. The wind shear however can be significant since there is little vertical
exchange. During these conditions large wind direction gradients can occur.
4.2.2 Influence of roughness
In neutral conditions and flat terrain with uniform roughness the vertical profile
can be described analytically by the power law:
1 1
2 2
( )
( )
u h h
u h h
a
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
(6)
Temperature
Height
Stable
Unstable
Neutral
Figure 10: Vertical temperature profile: changes of temperature with height.

u(h1) and u(h2) are the wind speeds at heights h1 and h2. This is merely an
engineering approximation where the wind shear exponent α is a function of
height z, surface roughness, atmospheric stability and orography. Therefore,
a measured wind shear exponent is only valid for the specific measurement
heights and location and should thus never be used for vertical extrapolation
of the wind speed, which is unfortunately very often done leading to erroneous
results.
More helpful is the logarithmic law (log law) which in flat terrain and neutral
conditions expresses the change of wind speed as a function of surface roughness:
*
0
( ) ln
u h
u h
z
k
⎛ ⎞
= ⎜ ⎟
⎝ ⎠
(7)
The wind speed u depends on the friction velocity u*, the height above ground
h, the roughness length z0 and the Kármán constant k, which equals 0.4. Applying
the above equation for two different heights and knowing the roughness length z0
allows the extrapolation of the wind speed to a different height:
( ) ( ) 2 0
2 2 1 1
1 0
ln( / )
ln( / )
h z
u h u h
h z
= (8)
The surface roughness length describes the roughness characteristics of the
terrain. It is formally the height (in m) at which the wind speed becomes zero
when the logarithmic wind profile is extrapolated to zero wind speed. A corre-
sponding system uses roughness classes.A few examples of roughness lengths and
their corresponding classes are given in Table 3.
In offshore conditions the roughness length varies with the wave condition,
which in turn is a function of wind speed, wind direction, fetch, wave heights
and length. However, recent surveys have shown that the vertical profile offshore
is heavily influenced by the effect of atmospheric stability [18], because the
roughness length offshore will in most cases be several orders of magnitude
smaller than onshore.
Table 3: Example surface roughness length and class.
Cover z0 (m) z0 as roughness class
Offshore 0.0002 0
Open terrain, grass, few isolated obstacles 0.03 1
Low crops, occasional large obstacles 0.10 2
High crops, scattered obstacles 0.25 2.7
Parkland, bushes, numerous obstacles 0.50 3.2
Regular large obstacle coverage
(suburb, forest)
0.5–1.0 3.2–3.7

4.2.3 Influence of atmospheric stability
The logarithmic law presented above can be expanded to take atmospheric stability
into account:
0
( ) ln
u h
u h
z
*
k
⎛ ⎞
⎛ ⎞
= − Ψ
⎜ ⎟
⎜ ⎟
⎝ ⎠
⎝ ⎠
(9)
Ψ is a stability-dependent function, which is positive for unstable conditions and
negative for stable conditions. The wind speed gradient is diminished in unstable
conditions (heating of the surface, increased vertical mixing) and increased during
stable conditions (cooling of the surface, suppressed vertical mixing). Figure 11
shows an example of the effect of atmospheric stability when extrapolating a
measured wind speed at 30 m to different heights.
4.2.4 Influence of orography
The term orography refers to the description of the height variations of the ter-
rain. While in flat terrain the roughness is the most dominant parameter, in hilly
or mountainous terrain the shape of the terrain itself has the biggest impact on
the profile.
Over hill or mountain tops the flow will be generally accelerated (Fig. 12). As a
consequence the logarithmic wind profile will be distorted: both steeper and then
less steep depending on height. The degree of distortion depends on the steepness
Figure 11: Wind profiles for neutral, unstable and stable conditions [16].

of the terrain, on the surface roughness and the stability. In very steep terrain the
flow across the terrain might become detached and form a zone of turbulent sepa-
ration. As a rule of thumb this phenomena is likely to happen in terrain steeper
than 30% corresponding to a 17° slope. The location and dimensions of the separa-
tion zone depend on the slope and its curvature as well as roughness and stability.
In cases of separation, the wind speed profile might show areas with negative ver-
tical gradient, where the wind speed is decreasing with height.
4.2.5 Influence of obstacles
Sheltering of the anemometer by nearby obstacles such as buildings leads to a dis-
tortion of the vertical profile. The effect of the obstacles depends on their dimen-
sions, position and porosity.
Figure 13 sketches out the reduction of wind speed behind an infinite long two-
dimensional obstacle. The hatched area relates to the area around the obstacle
which is highly dependent on the actual geometry of the obstacle. The flow in this
area can only be described by more advanced numerical models such as computa-
tional fluid dynamic (CFD) models.
4.3 Flow models
4.3.1 General
Due to the complexity of the vertical extrapolation described above, the predic-
tion of the variation of wind speed with height is usually calculated by a computer
model, which is specifically designed to facilitate accurate predictions of wind
farm energy. These models also estimate the energy variation over the site area
Figure 12: Effect of topography on the vertical wind speed profile gentle hill (top),
steep slope (bottom).

and using separate physics, the wake interaction between the wind turbines. The
use of such tools allows the energy production of different layouts, turbine types
and hub heights to be rapidly established once the model has been set up. Site flow
calculations are commonly undertaken using the WAsP model, which has been
widely used within the industry over the past decades.
However in the last few years use of CFD codes is increasing, although CFD
tools are typically used in addition to and not instead of more simple tools, to
investigate specific flow phenomena at more complex sites. CFD tools must be
used with care, as the results are quite sensitive to modelling assumptions and to
the skill of the user of the code. Typical use of CFD tools is, firstly, to give another
estimate of the local acceleration effects at the sites, and secondly, to identify hot
spots, in other words areas where the wind conditions are particularly difficult for
wind turbines. In particular, such tools are starting to be used to assist in the micro-
siting of wind turbines on more complex sites [1]. Often it is the only means by
which turbulence and shear across the site can be estimated.
4.3.2 WAsP
The challenge is to take a topographical map and the long-term corrected wind
climate at a known point and use this information to calculate the long-term wind
speed at all points on the map. WAsP is accomplishing this task using a double
vertical and horizontal extrapolation. The idea behind this is quite simple. The
local measurements on site are cleaned from local effects like obstacles, roughness
and orography to calculate the geostrophic wind climate. Having determined the
Figure 13: Reduction of wind speed in percent due to shelter by a two-dimensional
obstacle [17].

Figure 14: Principle of WAsP [17].
geostrophic wind climate this way in one position, the effect of the local obstacles,
roughness and orography of the wind turbines location can be calculated for a
different position assuming that the geostrophic wind climate is the same at this
position. This principle is indicated in Fig. 14.

zD displacement height
Problem areas
5 zD
WasP solution
Figure 15: Modelling forest in WAsP: displacement height [19].
As explained above the vertical profile also depends on the atmospheric stability.
Even at moderate wind speeds, deviations from the logarithmic profile occur when
the height exceeds a few tens of meters. Deviations are caused by the effect of
buoyancy forces in the turbulence dynamics; the surface roughness is no longer the
only relevant surface characteristic, but has to be supplemented by parameters
describing the surface heat flux. With cooling at night, turbulence is lessened,
causing the wind profile to increase more rapidly with height; conversely, daytime
heating causes increased turbulence and a wind profile more constant with height
(see Section 4.2.3).
In order to take into account the effects of the varying surface heat flux without
the need to model each individual wind profile, a simplified procedure was adopted
in WAsP which only requires the climatological average and root mean square of
the annual and daily variations of surface heat flux. This procedure introduces the
degree of ‘contamination’ by stability effects to the logarithmic wind profile when
conditions at different heights and surfaces are calculated [16].
It is important to appreciate that as the distance of the turbines from the meteo-
rological mast increases, the uncertainty in the prediction also increases. This
increase in uncertainty is typically more rapid in complex terrain than in simple
terrain. When developing a site the increased uncertainty should be reflected in
the number of measurement masts on site and the measurement height. As a
rule of thumb the measurement height should be minimum 2/3 of the planned
hub height.
A great challenge is the modelling of forests using WAsP. To model the wind
speed correctly in WAsP a so-called displacement height must be introduced
together with a very high roughness [19]. The displacement height is an artificial
increase of terrain height for the area covered by forest. It should be around 2/3
of the tree height depending on the tree’s density and the shape of the canopy.
At the edge of the forest the displacement height should taper off linearly out to
a distance of five times the displacement height (Fig. 15). The displacement height
shall correct for the speed up of the wind as the forest to some extent acts like an
artificial hill leading to accelerated flow across the forest. The roughness length to
be applied for forested areas should be in the order of 0.4 to more than 1 m. The
increased roughness will lead to a wind profile exhibiting a higher shear when
modelling the forest [19]. This approximation is only valid in simple terrain.

One of the key simplifications of WAsP allowing this double extrapolation is the
linearisation of the Navier-Stokes equations. This simplification is the main reason
for WAsP’s limitation in complex terrain where the terrain slope exceeds 30° and
flow separation is likely to occur. Several studies have shown that WAsP tends to
make prediction errors in complex terrain [20]. Figure 16 shows that due to the
separation zones the stream lines follow the shape of a virtual hill with a somewhat
reduced steepness. However, WAsP uses the real hill steepness to calculate the
speed-up and is thus over-predicting the wind speed.
A correction of this model bias is possible using the so-called Ruggedness Index
(RIX) which is defined as the percentage of the area around an object that has
steepness above 30% (corresponds to 17° slope), thus the WAsP model assump-
tions are violated (Table 4). Figure 17 shows the relationship between expected
wind speed error and the difference in complexity between the position of the
measurement mast and the position of the future wind turbine. If the reference site
(measurement site) is less rugged and the predicted sites (WTGs) are very rugged,
the difference ΔRIX will be positive, and thus according to several studies an over-
prediction of the wind speed can be expected. If the reference site (measurement
site) is more rugged and the predicted sites (WTGs) are less rugged, the difference
ΔRIX will be negative, and thus according to several studies an under-prediction
of the wind speed can be expected. The model bias will vary from site to site. It must
be emphasised that the ΔRIX description is a very much simplified description of the
complexity variations in the terrain and that more data analysis is needed like that
shown in Fig. 17.
Steepness ~ 40%
Virtual Hill
Steepness ~ 30%
-100 0 100
120
80
40
Figure 16: Effect of a steep hill – flow separation [21].
Table 4: Strengths and weaknesses of WAsP.
Strength Weakness
Easy to use Valid only for near-neutral conditions
Cheap and fast Problems in complex terrain with flow
separation
Validated, limitations are known and
can be dealt with
Valid only for surface layer

5 Siting and site suitability
5.1 General
During the process of siting (also referred to as micro-siting), the locations of the
future wind turbines are determined. Apart from the wind resource this process is
driven by a number of other factors such as technical risks, environmental impact,
planning restrictions and infrastructure costs.
Technical risks can very often be mitigated by adjusting the layout to suit the
site-specific conditions. High turbulence as a main driver of fatigue loads can be
avoided by maintaining sufficient distances between the turbines, keeping clear of
forests and other turbulence-inducing terrain features like cliffs. Steep terrain
slopes should be avoided to reduce stresses on the yaw system and blades and
thereby improve the energy output.
5.2 Turbulence
5.2.1 Ambient turbulence
The turbulent variations of the wind speed are typically expressed in terms of the
standard deviation su of velocity fluctuations. This is measured over a 10-min period
and normalised by the average wind speed è
—
and is called turbulence intensity Iu:
u
u
I
U
s
= (10)
Figure 17: WAsP wind speed prediction error as a function of difference in rug-
gedness indices between the predicted and the predictor site [20].

The variation in this ratio is caused by a large natural variability, but also to
some extent because it is sensitive to the averaging time and the frequency response
of the sensor used.
Two natural sources of turbulence can be identified: thermal and mechanical.
Mechanical turbulence is caused by vertical wind shear and depends on the surface
roughness z0. The mechanically caused turbulence intensity at a height h in neutral
conditions, in flat terrain and infinite uniform roughness z0 can be described as
0
1
ln( / )
u
I
h z
= (11)
This equation shows an expected decrease of turbulence intensity with increas-
ing height above ground level.
Thermal turbulence is caused by convection and depends mainly on the tem-
perature difference between ground and air. In unstable conditions, with strong
heating of the ground, the turbulence intensity can reach very large values. In sta-
ble conditions, with very little vertical exchange of momentum, the turbulence is
generally very low. The impact of atmospheric stability is considerable in low to
moderate wind speeds.
The turbulence intensity varies with wind speed. It is highest at low wind speeds
and shows an asymptotic behaviour towards a constant value at higher wind speeds
(Fig. 18). Typical values of Iu for neutral conditions in different terrains at typical
hub heights of around 80 m are listed in Table 5.
Forests cause a particularly high ambient turbulence and require special atten-
tion (Table 5). While special precautions allow estimating the mean wind speed in
or near forest with standard flow models, the turbulence variations can only be mod-
elledwithmoreadvancedmodels.Differentconceptsareavailableformoreadvanced
CFD codes. One method to model forest is the simulation via an aerodynamic drag
Figure 18: Turbulence versus wind speed (onshore).

term in the momentum equations, parameterised as a function of the tree height
and leaf density. The turbulence model might also be changed to simulate the
increased turbulence.
As a rule of thumb measured data indicates that the turbulence intensity created
by the forest is significant within a range of five times the forest height vertically
and 500 m downstream from the forest edge in a horizontal direction. Outside
these boundaries the ambient turbulence intensity is rapidly approaching normal
values [22].
In or near forest, the mechanically generated turbulence intensity increases in
high wind speeds due to the increasing movement of the canopy. A similar phe-
nomenon can be observed in offshore conditions where increasing waves lead to
increased turbulence in high wind speeds (Fig. 19). The increasing turbulence with
increasing wind speed is of great importance when calculating the extreme gusts
for a site.
Table 5: Typical hub height turbulence intensities for different land covers.
Land cover Typical Iu (%)
Offshore 8
Open grassland 10
Farming land with wind breaks 13
Forests 20 or more
Turbulence
intensity
Windspeed (m/s)
0 5 10 15 20 25 30
0.08
0.1
0.12
0.14
0.06
8 m
48 m
Figure 19: Turbulence versus wind speed (offshore) [23].

5.2.2 Wake-induced turbulence
The wake of a wind turbine is characterised by the velocity deficit in comparison to
the free flow and an increased level of turbulence. While the velocity deficit leads
to a reduced energy output (Fig. 23) the turbulence in the wake leads to increased
mechanical loads. The wake-induced turbulence Iwake is of a different nature than
the ambient turbulence I0 as the length scales are different. The wind turbine has to
withstand both types of turbulence added up depending on its class (Table 6):
2 2
windfarm 0 wake
I I I
= + (12)
Figure 20 shows the empirically measured, maximum wake turbulence as a
function of distance between two wind turbines expressed in rotor diameters. As a
rule of thumb wind turbines should not be closer than the equivalent of 5 rotor
diameters in the main wind direction. Perpendicular to the main wind direction, a
minimum distance of 3 rotor diameters should be maintained.
Turbulence particularly affects the blade root flap-wise as well as leading to
varying torsion in the main shaft which is fed through the gearbox into the genera-
tor. Additionally turbulence causes thrust loads in the tower. The international
Table 6: Definition of IEC classes [25].
Class
I II III IV S
Uref (m/s) 50 42.5 37.5 30 Site-specific
Uave (m/s) 10 8.5 7.5 5
0.0
0.1
0.2
0.3
0.4
0.5
0 2 4 6 8 10
Separation in Diameters
I_w
I_w_mod
Andros
Taff Ely
AlsVik
Vindeby
Figure 20: Wake-induced turbulence intensity Iwake [24].

design standard IEC [25] assumes a maximum level of total turbulence of 18% for
a wind turbine designed for high turbulence.
5.3 Flow inclination
A layout should aim for a high energy yield. Since the flow is accelerated across
hills one idea could be to place the wind turbines at the point with the highest
acceleration which is typically at the location of greatest curvature of the slope of
the terrain. The unwanted side effect of such a location is that the flow might not
be horizontal as it follows the shape of the mountain, thus the inflow angle is not
zero. The askew wind vector leads to lower production as the angular response of
the wind turbine can be simplified as a cosine and thus the horizontal component
of the wind mainly contributes to the energy generation.
Additionally the wind turbine will be subject to higher loads when being exposed
to large inflow angles. The fatigue loads on the blades will increase since the angle
of attack changes during one rotation. Furthermore, the bending loads of the rotat-
ing parts of the drive train are increased, and finally the yaw drive will be subject
to extra loads due to the uneven loading of the rotor. Thus a good layout avoids
these locations. Figure 21 shows an example where the turbine position has been
moved back from the edge of the cliff to a position with a much lower inflow angle
(and slightly lower wind speed).
Information about the flow inclination can be obtained using ultrasonic or pro-
peller anemometers with the restrictions described earlier. However, the obtained
data only reflects a single location. Only flow models are capable to evaluate the
flow inclination for each wind turbine location.
Care has to be taken, though when moving too far back from cliff edges, as this
area is prone to separation leading not only to increased turbulence but in the worst
Terrain slope
Flow slope
Figure 21: Example of speed-up and inflow angle across a mountain.

case to reverse flow imposing the potential of serious damage to the wind turbine
(Fig. 22). The vertical and horizontal extent of such unsuitable areas can be
estimated using CFD.
The international design standard IEC [25] assumes an inflow angle of maximum
8° for load calculations.
5.4 Vertical wind speed gradient
The loads on the rotor depend on the wind speed difference between the bottom
and the top of the rotor. The gradient is normally expressed by the wind shear
exponent a from the power law (eqn (6)). Most load cases assume a gradient of 0.2
between the bottom and the top of the rotor [25]. Note that in some cases, loads
will be larger for very small or negative a.
The gradient causes changing loads of the blades as the angle of attack changes
with each rotation. Thus the gradient adds to the fatigue loads of the blade roots.
Furthermore the rotating parts of the drive train are stressed. The gradient is
affected by four different phenomena:
• Terrain slope. The logarithmic profile can be heavily distorted by terrain slopes.
While in flat terrain the wind speed increases with height, steep slopes might
lead to a decrease with height. This is particularly likely for sites where the flow
separates and does not follow the shape of the terrain anymore (Fig. 12). As a
consequence the wind speed exponent might exceed the design limit in some
sections of the rotor.
• Roughness/obstacles. If wind turbines are located closely behind obstacles like
for example a forest the vertical wind speed profile might be again heavily dis-
torted and areas of the rotor are exposed to large gradients. The degree of defor-
mation of the profile depends not only on the geometry of the obstacle but also
on its porosity.
• Layout. As explained above, the wake of a wind turbine is a conical area behind
the rotor with increased turbulence and reduced wind speed since the rotor
of the wind turbine has extracted kinetic energy from the flow. Figure 23 shows
the effect of the wake on the profile, where the dotted line represents the free
flow and the straight line the profile 5.3 rotor diameter behind the wind turbine.
Figure 22: An example of flow separation over a cliff.

It can clearly be seen that some areas of the deformed profile show very large
gradients. If the wind turbines operate in part wake situations (Fig. 24), the wind
turbines behind the front row will not only be exposed to vertical gradients but
also to horizontal gradients which add significantly to the loads.
• Atmospheric stability. As explained above, the vertical wind speed profile de-
pends on the vertical temperature profile. With increasing heating of the ground
(unstable conditions) the turbulence increases and therefore the profile is being
“smoothed” out. This leads to a steep wind speed profile characterised by very
little increase of wind speed with height. Stable conditions in contrast are
characterised by a very flat profile with significant wind speed gradients (see
Fig. 11). Depending on the prevailing atmospheric stability of the site the
gradient of 0.2 assumed for most load cases can be exceeded. Additionally in
stable conditions significant wind direction shears are common.
Figure 24: Horizontal gradient due to part wake operation.
Figure 23: Vertical wind profile in front and behind a wind turbine [16].

An optimised layout can avoid excessive gradients related to the first three phe-
nomena by staying clear from steep slopes, obstacles and allow a sufficient spacing
between the wind turbines.
The gradient is often determined by measurements. Care has to be taken when
the measurement height is lower than hub height since the wind shear exponent is
a function of height, thus a gradient measured at lower height is not representative
for higher heights. Be also aware of the fact that because the measured wind speed
differences can be relatively small, the resulting measurement uncertainties can
become quite significant. The preferred method in these cases is to model the wind
speed profile using flow models.
6 Site classification
6.1 Introduction
When designing wind turbines a set of assumptions describing the wind climate
on-site is made. As the robustness of a wind turbine is directly related to the costs
of the machine, a system has become common of grouping sites into four different
categories to allow cost optimisation of the wind turbines. The site class depends
on the mean wind speed and extreme wind speed at hub height referred to as IEC
classes [25]. Furthermore three different turbulence classes have been introduced,
for low, medium and high turbulence sites. The term extreme wind or Uref is used
for the maximum 10-min average wind speed with a recurrence of 50 years at hub
height. Please note that Uref is not related to the mean wind speed.
6.2 Extreme winds
Selecting a suitable turbine for a site requires knowledge about the expected extreme
wind in the form of the maximum 10-min average wind speed at hub height with a
recurrence period of 50 years (Uref according to the IEC 61400-1 [25]). While local
building codes frequently generalise the amplitude of extreme events across large
areas, on-site measured wind data allows for a more precise site-specific predic-
tion of the expected 50-year event. A number of methods offer the possibility to
estimate the on-site 50-year maximum 10-min wind speed from available shorter
term on-site data. The IEC 61400-1 does not prescribe any preferred method for
this purpose though.
Under certain assumptions, a Gumbel analysis [26] can be applied to predict the
50-year extreme wind speed on the basis of the measured on-site maximum wind
speeds. If these assumptions are correct, then the measured maximum wind speeds
should resemble a straight line in the Gumbel plot.
Different methodologies are available to extract extreme events from a
short-term data set and further to fit the Gumbel distribution to these extremes.
The parameters describing the Gumbel distribution are generally determined
from the linear fit in a Gumbel plot. Hereby the so-called reduced variant

(transformed probability of non-exceedence) is plotted versus wind speed.
The reduced variant (probability) can be expressed in different ways leading to
different plotting positions. Furthermore, a variety of fitting options are avail-
able leading to a vast number of different results [27]. However, all methods
have one problem in common. The resulting 50-year estimate is highly corre-
lated to the highest measured wind speed event of the time series used for the
analysis [28].
The European Wind Turbine Standard, EWTS [29] offers an option to estimate
the extreme wind based on the wind speed distribution on site rather than a mea-
sured time series. It suggests a link between the shape of the wind distribution and
the extreme wind. The EWTS relates a ratio determined by the Weibull k factor to
the annual average wind speed to estimate the 50-year extreme wind speed. This
factor is 5 for k = 1.75, <5 for higher values of k and >5 for lower k values (i.e. high
values for distributions with long tails, and low values for distributions with lower
frequency of high wind speeds). The extreme wind is calculated as this factor
times the yearly averaged wind speed.
The extreme 3-s gust or also called design wind speed of the wind turbine (Ue50)
is a function of the Uref and the turbulence intensity. The extreme gust is estimated
using the relationship, where the turbulence intensity Iext is a high wind speed
turbulence value estimated from the measured data:
ext
e50 ref(1 2.8 )
U U I
= + (13)
This relationship is based on experimental data as well as theoretical work (Fig. 25).
As mentioned in Section 5.2.1 care has to be taken in offshore and forest situa-
tions as the turbulence intensity does not show asymptotic behaviour but increases
with increasing wind speed. It is thus much more difficult to estimate Iext under
these conditions.
Figure 25: Measured gust as a function of wind speed [16].

7 Energy yield and losses
7.1 Single wind turbine
The power production varies with the wind speed that strikes the rotor. The wind
speed at hub height is normally used as a reference for the power response of the
wind turbine. Knowing the power curve of a wind turbine P(u), the mean power pro-
duction can be estimated using the probability density function of the wind speed at
hub height f(u), which is typically expressed as a Weibull distribution (see eqn (3)):
1
0 0
( ) ( )d exp ( )d
k k
k u u
P f u P u u P u u
A A A
−
∞ ∞
⎛ ⎞
⎛ ⎞ ⎛ ⎞
= = −
⎜ ⎟
⎜ ⎟ ⎜ ⎟
⎝ ⎠ ⎝ ⎠
⎝ ⎠
∫ ∫ (14)
This integral cannot be computed analytically and thus has to be solved numer-
ically. For this purpose the power curve is divided into a sufficient number of lin-
ear sections, typically for 0.5 m/s steps. The power output can now be calculated
by summing up the produced energy for each wind speed bin.
Care has to be taken as the power of the wind is proportional to the air density.
A power curve normally refers to an air density of 1.225 kg/m3
which corresponds
to a temperature of 15°C at sea level. A higher elevation and/or a warmer site will
lead to a lower air density and thus to a lower energy output.
Special considerations should be given to the fact that a number of site-specific
parameters influence the power curve and thus the energy yield most significantly
turbulence and wind shear [30].
Another way of stating the annual energy output from a wind turbine is to look
at the capacity factor for the turbine in its particular location. By capacity factor
we mean its actual annual energy output divided by the theoretical maximum out-
put if the machine were running at its rated (maximum) power during all of the
8760 h of the year. The capacity factors may theoretically vary from 0 to 100%, but
in practice they will mostly be around 30–40%.
7.2 Wake and other losses
As mentioned in Section 5.2.2 and shown in Fig. 23 the wake of a wind turbine
is characterised by a velocity deficit. This will lead to a reduced efficiency of
any wind turbine operating in its wake. The so-called park loss is dependent on the
thrust curve of the rotor, on the wake decay constant (which in turn is a function
of the ambient turbulence intensity), and the distance between the turbines. High
ambient turbulence intensity will lead to an increased mixing between the wake and
the surrounding undisturbed flow. As a consequence the opening angle of the wake
increases, thus resulting in smaller park losses than low turbulence situations.
Two models are commonly used in the industry, the N.O. Jensen model and the
Eddy-Viscosity model of Ainslie [32]. The N.O. Jensen model is a simple, single
wake kinematic model, in terms of an initial velocity deficit and a wake decay
constant. It is based on the assumption that the wake right behind the wind turbine

has a starting diameter equal to the rotor diameter and is linearly expanding as a
function of the downwind distance. The Ainslie model is based on a numerical
solution of the Navier-Stokes equations with an eddy viscosity closure in cylindri-
cal coordinates. The eddy viscosity is described by the turbulent mixing due to the
induced turbulence, generated within the shear layer of the wake, and the ambient
turbulence [33].
Other sources of losses could be related to availability, electrical losses, high
wind hysteresis, environmental conditions like icing and blade degradation and
curtailments, of which wind sector management is the most common one. As
explained earlier turbine loading is influenced by the wake effects from nearby
machines. For some wind farms with particularly close spacing, it may be neces-
sary to shut down certain wind turbines for certain wind directions. Typically this
is done in a wind climate with a very dominant and narrow main wind direction.
7.3 Uncertainty
Uncertainty analysis is an important part of any assessment of the long-term energy
production of a wind farm. The most important contributors to the uncertainty are:
on-site wind speed measurements
•
long-term correction
•
flow modelling
•
performance of the wind turbine
•
park losses
•
Behind each point a vast number of aspects have to be considered and quantified
including anemometer calibration, mounting effects, inter-annual variability, qual-
ity of the long-term data, vertical and horizontal extrapolation, quality of the topo-
graphic input, power curve uncertainty, wake model, etc. Some inspiration can be
found in [34].
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