Micro wind turbine performance in urban environments

Building Serv. Eng. Res. Technol. 32,3 (2011) pp. 245–262
Micro wind turbine performance under real weather
conditions in urban environment
A Glass BEng(Hons) and G Levermore BSc ARCS PhD DIC FCIBSE
School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, UK
The aim of this article is to evaluate the performance of micro wind turbines in a built-up
environment. For this purpose, five independent micro wind turbine systems, consisting of
two distinctly different models, were tested and evaluated under real life conditions over a
period of 12 months. This article provides an overview of the experimental set-up used to
test the two different micro wind turbines and then goes on to present the basic
background theory for horizontal axis micro wind turbines and the variation of coefficient of
performance with wind speed. The wind potentials at the test site were assessed to
determine the theoretical outputs of the turbines which were compared with the measured
outputs over a year. The measured outputs were disappointingly low. One reason for this is
turbulence, for which directional turbulence (lateral turbulence) has been shown to be a key
indicator, better than the standard wind speed (longitudinal) turbulence. Another factor is
the inverter efficiency and power consumption, which is not negligible. Finally the
theoretical paybacks under the 2010 Feed-in Tariffs were calculated along with estimated
carbon savings.
Practical application: Renewables such as wind turbines are increasingly being designed
and installed to help achieve lower carbon buildings. The output of micro turbines,
however, can be disappointing due to lateral turbulence and inverter consumptions. These
factors are explained so that designers can be aware and assess the likely outputs more
accurately.
Symbols
¼air density (kg/ms1)
A ¼swept area of turbine (m2)
U0 ¼air speed (m/s)
P0 ¼power contained by wind (W)
PT ¼power generated by turbine (W)
Cp ¼coefficient of performance
R ¼gas constant
T ¼temperature (8C)
P ¼pressure (Pa)
(. . .)v ¼vapour
(. . .)d ¼dry air
x ¼mean of data
SD ¼standard deviation of data
n ¼number of data points
1 Introduction
After agreeing to the Kyoto protocol, the UK
government has accepted targets to lower its
greenhouse gas emissions by 80% until 2050.
As a result, targets were set to generate 10%
of electricity demand from renewable sources
by 2010, and 20% by 2020.1 In 2003, UK
Address for correspondence: Geoffrey Levermore, School of
Mechanical, Aerospace and Civil Engineering, University of
Manchester, Manchester, UK.
E-mail: geoff.levermore@manchester.ac.uk
The Chartered Institution of Building Services Engineers 2010 10.1177/0143624410389580

246 Micro wind turbine performance
housing was responsible for 30% of the total
energy consumption within the country.2
In order to tackle the environmental issues
in the UK domestic sector, the UK govern-ment
has issued the Code for Sustainable
Homes3 (CSH), and demands that all new
homes as of 2016 should be built to CSH level
6 (zero-carbon) standards.4
In an attempt to investigate how much
energy can be generated from on-site micro
renewable energy systems in order to satisfy
CSH level 6 requirements, Barratt PLC con-structed
the EcoSmart Show Village in
Chorley, Lancashire, in 2006. It consisted of
seven test homes which featured 2006 energy-efficiency
and renewable energy technologies,
including micro wind turbines. At that time
there were only few investigations into micro
wind turbines in the urban environment, for
example by Clausen et al.,5 who saw potential
for micro wind but concluded that technology
had not reach the maturity of larger turbines.
Some of the problems associated with wind
generating in urban environments had been
explored in greater detail, in particular the
effect of turbulence in urban canyons.
Eliasson et al.6 measured counter-rotating
vortices within the canyons, wind shear
along canyon edges and high degrees of
turbulence even at low wind speeds. Wind
tunnel simulations7 showed strong evidence
that sharp flow accelerations develop around
roof tops, causing high fluctuations in hori-zontal
velocities.
To gain a better understanding about the
performance of the micro renewable energy
systems, they were evaluated over a 15-month
test period under real weather conditions.
Weather conditions were monitored and
recorded using an on-site weather station.
Figure 1 shows a model of the test site.
Parallel to this investigation, several other
studies were conducted to test micro wind
turbines in the urban environment, including
the Warwick wind trials and the WINEUR
project. From these studies it was concluded
that urban wind turbines faced several prob-lems,
such as turbulence, which was found to
reduce output by 15–30%.8 It was further
shown that the capacity factor was only
around 4–6.4%, compared to around 10%
for rural sites. The WINEUR project specif-ically
suggested9 minimum requirements to
make urban wind generation viable, including
1b
2b
2a
1a
WS
2c
W N
S E
Figure 1 Photograph showing a model of the test site, where WS refers to weather station

average wind speeds above 5.5 m/s, the tur-bine
to be mounted on a building 50% higher
than surroundings and at a hub height at least
30% greater than building height. This is also
confirmed by a CFD (computational fluid
dynamics) analysis conducted by Heath
et al.,10 showing that for a typical urban
layout of buildings a hub height of at least
50% above building height is required to
capture wind that is not significantly affected
by surrounding buildings. Further studies
have been conducted to show the viability of
urban micro wind turbines. Financial pay-back
estimates range from 170 to 240 years11
for a range of wind data from Turkey, to
30–90 years12 in the UK using a model that
accounts for wind shear and terrain correc-tion.
A different approach to life-cycle anal-ysis
was taken by Allen et al., who calculated
the energy payback to be 9 years using a
micro wind turbine system model including
inverter.
In November 2008 the Planning and
Energy Act13 set out a series of requirements
for the UK Government to meet its commit-ments
to combat climate change, in particular
by encouraging the use of renewable energy
systems to generate power. As a result of the
Planning and Energy Act, renewable energy
Feed-in tariffs14 (FIT) have been introduced
in April 2010, which are incentives for
installing renewable energy systems such as
Wind Turbines.
1.1 Micro wind turbines at EcoSmart
show village
The experimental set-up consisted of five
micro wind turbines. All turbines were
mounted on the roof edges of test homes
within the EcoSmart Show Village. The
turbines were installed with around 1.5–2m
clearance from the roof top, which is similar
to any private micro wind turbine arrange-ment.
The effective hub height of the turbines
is around 10 m. The weather station used to
A Glass and G Levermore 247
record wind data was mounted in a similar
position on the roof of one of the test homes.
Two of the five turbines were type 1
turbines, a 1kW rated 3-blade turbine. The
other three turbines were type 2 turbines, a
0.4kW rated 5-blade turbine. Specifications
for both turbines are summarised in Table 1
below. The turbines were used in conjunction
with an inverter, which had similar power
ratings to the turbines.
In addition to the parameters in Table 1,
power curves, which show the variation of
energy generation for different wind speeds,
have also been supplied by the manufacturer.
These are shown in Figure 2 for both turbine
1 and turbine 2. The cut-in speed, depending
on the required start-up torque,15 is 3 m/s for
both turbines.
2 Wind turbine theory
2.2 Power coefficient
Assuming the turbine is constantly point-ing
into the wind, linear momentum theory
states that the power of the wind moving
through the turbine rotor is given by
Equation (1):
P0 ¼
1
2
AU3
0 ð1Þ
However, the power that can be generated
by the rotor differs from that contained by the
wind. This is shown by simple observation
Table 1 Wind turbine specifications
Turbine 1 Turbine 2
Diameter (m) 1.75 1.1
Area (m2) 2.4 0.95
Rated power (kWh) 1.0 0.4
No. turbine blades 3 5
Cut-in speed (m/s) 3.0 2.5
Cut-off speed (m/s) 12.5 16
Capital Cost 2006 £1500 £2250
Warranty (yrs) 10 1

9 10 11 12 13 14 15 16 17 18
that the air is still moving away from the rotor
after it has passed through it. This means that
there is still some energy left in the air,
allowing it to carry on moving. Hence,
another term needs to be introduced to the
above equation. This term is called the power
or performance coefficient (Cp) and essen-tially
determines the efficiency at which
energy is extracted from the wind. The
power generated by the turbine is therefore
given by Equation (2):
PT ¼ CpP0 ð2Þ
This Cp value typically varies with wind
speed and is different for each turbine design.
According to Betz’ law, the Cp may achieve a
maximum value of 0.59, assuming perfectly
efficient machinery.
The power coefficient, Cp, can be deduced
from the power curve of the wind turbine.
Equations (1) and (2) can be used to relate Cp
to the wind speed, as shown in Equation (3):
CP ¼
2PT
AU30
ð3Þ
where A is the rotor area stated in Table 1
The power values can be determined from
the power curves shown in Figure 2, where
the wind speed U0 acts as a control variable.
Results for CP variation with wind speed are
shown in Figure 3.
Turbine 1 shows a fairly consistent CP
value around 0.4, which only shows some
slight variations between 3 m/s and 7 m/s.
Turbine 2 on the other hand shows a consid-erably
higher CP value for 3 m/s to 7 m/s,
beyond which it begins to fall off continu-ously,
until reaching a value of around 0.2 at
the cut-off speed.
This difference in CP variation is largely a
result of the number of blades of the different
turbine models. The CP value mainly depends
on the tip-speed ratio of the turbine blades,
which is the ratio of rotational speed over
wind speed. If the blades move too slowly in
comparison to wind speed, a large part of
the wind will pass through the turbine
blades without losing any of its energy.
If on the other hand blades move too fast,
then the turbulent air from one blade will
affect the next blade, reducing aerodynamic
efficiency. The tip-speed ratio is a function
of the number of blades. Hence a turbine with
a large number of blades, such as turbine 2,
will work best at low wind speeds, while a
turbine with fewer blades, such as turbine 1,
will perform better at higher wind speeds.
However, the controller of the turbines as well
1200
1000
800
600
400
Output (W)
200
0
0 1 2 3 4 5 6 7 8
Wind speed (m/s)
Turbine 1
Turbine 2
Figure 2 Power curve for 1 kW rated turbine 1 and 400W rated turbine 2

as blade design16 also plays a part in deter-mining
the CP value, as the actual tip-speed
ratio may vary depending on these
factors.
2.3 Wind potentials at test site
Before finding the theoretical output of the
wind turbines, the wind potential at the test
site must first be established. Weather data,
including wind speed, wind direction, atmo-spheric
pressure and humidity have been
recorded directly at the site at 10-min inter-vals
in accordance with BS EN 61400-
21:2002. After testing was completed, the
revised BS 61400-21:2008 was released, which
recommends using measurements at 1-min
intervals. While this would have vastly
improved the analysis it could not be antic-ipated
at the outset of the research in 2006.
Data were acquired using the Davis Vantage
Pro 2 Plus weather station, which is a
combined package including an anemometer,
wind vane, solar panel, temperature and
humidity sensors. The accuracy of the ane-mometer
and wind vane is given by the
manufacturer as 2% for wind speed and to
78 for wind direction. However it must be
noted that the rather compact mounting
arrangement is likely to result in more
9 10 11 12 13 14 15 16 17
significant inaccuracies due to wind distortion
by the mounting pole. Additionally, the
weather station will experience different tur-bulence
patters to those experienced by tur-bines
2a, 2b and 2c, which is a result of the
different roof orientation. The wind data for
the test site is as shown in Table 2, derived
from wind software.17
Figure 4 provides a monthly breakdown of
the recorded wind speeds, giving mean, daily
high, daily low, as well as maximum and
minimum wind speeds recorded for each
particular month. Figure 5 provides the
wind speed frequency distribution.
Interestingly, the most common wind speed
that was recorded at the test site is 0 m/s. This
leads to suggest that the anemometer
response is poor at very low wind speeds.
This has a negligible effect on the present
research, as the affected range of wind
Cp
0.6
0.5
0.4
0.3
0.2
0.1
0
0 1 2 3 4 5 6 7 8
Wind speed (m/s)
Turbine 1
Turbine 2
Figure 3 CP Variation with wind speed
Table 2 Results from weather data analysis
Variable Value
Mean wind speed (m/s) 3.1
Min. wind speed (m/s) 0
Max. wind speed (m/s) 18.3
Hours of peak wind speed 22
Mean power density (W/m2) 53

20
15
10
Average value (m/s)
5
0
12000
10000
J F M A M J J A S O N D A
speeds is well below the turbine cut-in speed
of 3 m/s.
The wind rose plot in Figure 6 appears to
show some evidence of a prevailing wind
direction from the south and west directions.
However, there were buildings situated to the
south, to the north and at some distance to
the east of the weather station. All buildings
are of approximately the same height.
2.4 Density adjustment
Air density was not measured directly. As it
forms a part of the wind power Equation (1),
it must be found using atmospheric pressure,
humidity and temperature readings.
The ideal gas law can be combined with the
molecular density relationship to form
Equation (4):
¼
P
RT
ð4Þ
However, when determining the density of
air, the water vapour contained by the air
must also be considered. This essentially
forms a mix of two gasses, dry air and
vapour. Hence, the density of air can be
expressed by Equation (5):
¼

Pd
RdT
þ

Pv
RvT
ð5Þ
Frequency
8000
6000
4000
2000
0
0 1 2 3 4 5 6 7 8 9
m/s
10 11 12 13 14 15 16 17 18
Figure 5 Frequency distribution of wind speeds recorded at the test site
Max
Daily high
Mean
Daily low
Min
Figure 4 Monthly variation of wind speeds (Windographer)

The pressure values can then be determined
using relative humidity (RH), which is defined
as the ratio (expressed as a percentage) of the
actual vapour pressure to the saturation
vapour pressure at a given temperature.18
2.5 Theoretical energy generation
Having established the wind potentials and
wind turbine properties, it is now possible to
estimate the theoretical energy generation of
the turbines over the 12-month test period.
To account for the variation in power
coefficient CP, the method outlined previ-ously
has been used to find average CP values
for different wind speed bands. Results are
shown in Table 3.
90°
45°
135°
Using the adjusted CP values and Equation
0°
180°
(2), the theoretical output was calculated
using the following steps:
(1) Calculate air density for each interval of
weather data
(2) Calculate the power output of both
turbines for each interval of weather
data
(3) Using the power output, find the total
annual energy generation for both
turbines
Table 4 provides a summary of the theo-retical
total annual energy output, as well as
theoretical average daily output for both
turbines.
As expected, the estimated output of the
larger turbine (turbine 1) is considerably
greater than the estimated output of the
225°
270°
315°
Figure 6 Wind rose plot of wind speeds recorded at the test site
Table 3 Summary of Cp values at given wind speeds bands
Cp Turbine 1 Turbine 2
Cut-in – 4 m/s 0.21 0.54
4 m/s to 7 m/s 0.34 0.48
7 m/s to 10 m/s 0.36 0.36
10 m/s – Cut-off 0.35 0.17
Table 4 Estimated theoretical wind energy generation
Manually calculated estimate
Annual output (kWh) Avg. daily output (kWh)
Turbine 1 203 0.56
Turbine 2 100 0.27

smaller turbine (turbine 2). However, this
difference is disproportionate to the rated
power of the systems. While turbine 2 has
only 40% of the power rating and swept area
of turbine 1, its high efficiency at low wind
speeds means that in theory it is able to
generate around 50% of the energy that is
being generated by the larger turbine 1.
This theoretical output does not account
for any losses that may occur due to turbu-lence,
or while the turbine is turning into the
wind.
2.6 Inverters
Nearly all small-scale wind turbine systems
use a permanent-magnet generator, the
output of which is rectified to give a DC
voltage which varies with speed. Even in the
larger sizes the trend is toward permanent-magnet
machines. This is because, compared
to induction motors, they are more efficient
and effective over a wider speed range, and
this is especially so on the smaller scale.
Inverters are required to change the DC
power generated by the generator of the
turbine to AC power that can either be
exported to the grid, or be used directly by
appliances.
In its simplest form, an inverter uses a
transformer and a switch on the primary coil
to allow current to flow in opposite direc-tions,
causing the induction of alternating
current in the secondary coil.
The switching mechanism in inverters,
which is required to change the direction of
DC current, is called ‘commutation’. This can
be controlled in two ways, by self-commuta-tion
or forced commutation. The main dif-ference
is that forced commutation, or line/
network-commutation, allows the switch to
control the ‘on’ setting by using a device such
as a thyristor, while the ‘off’ setting is
controlled by a supplementary circuit.19
A self-commutated inverter on the other
hand can control both ‘on’ and ‘off’ settings.
With modern semi-conductor switching
devices, such as IGBT (Insulated Gate
Bipolar Transistor) or MOSFET (Metal
Oxide Semiconductor Field Effect
Transistor), high switching frequencies
exceeding several kHz are reached, which
makes it much easier to filter harmonics,
resulting in low network disturbances.20 This
property makes self-commutated inverters
more applicable to small-scale, grid-con-nected
renewable energy systems such as PV
and Micro Wind Turbines. They can either be
voltage commutated or current commutated,
meaning the switching is controlled by either
voltage or current levels. A survey has shown
that practically all inverters used for peak
loads of 1kWh are self-commutated, volt-age
type inverters.9
Any inverter, whatever its type, requires a
low-voltage control system, and most modern
systems will employ a microprocessor. The
power needed to drive the electronics is
usually obtained from the AC mains by
stepping down and rectifying using further
devices to give a stabilised low-voltage power
supply. It is unlikely that this can be done
without consuming at least 5W, so that, even
if the inverter is not switching (i.e. is not
passing power into the mains) it will consume
about 120Wh per day if it is left connected
and operational. If the inverter starts to
switch, then losses occur in each switching
operation additional to those already men-tioned.
It is impossible to generalise but some
feel that if operating at its rated output, the
inverter is unlikely to be more than about
90% efficient, with most of the losses being
attributable to switching. Hence it is quite
possible that the DC link needs to input a
power of about 2% of the inverter rating
before any measurably significant power is
fed into the mains.
3 Measured output
Before the energy generation of the wind
turbine systems can be determined accurately,

the energy consumption of the inverters must
be considered. The electricity meters installed
at the EcoSmart show village were designed
to measure electricity flowing both ways,
hence recording both power consumption
and power generation. In order to determine
the energy consumption of the inverters,
3 days were analysed, which were known to
have extremely low wind speeds. After ana-lysing
the weather data, the 8th, 19th and 21st
of July 2007 were found to have wind speeds
consistently below 2.5 m/s, which is the cut-in
speed of the smaller turbine (turbine 2). The
total daily energy readings for the wind
turbine meters on those particular days are
presented in Table 5.
The values in Table 5 were then used to
find the annual inverter energy consumption
by multiplying the daily value by the number
of days during which the turbines were
operating, in order to extrapolate the results
over the entire test period. Table 6 shows the
measured output as well as the estimated
annual power consumption of the inverter
unit, and recorded system downtime.
Table 6 shows that the energy output of the
wind turbines over the 12-month period
between 24/10/2006 and 27/10/2007 was
much lower than the expected theoretical
value shown in Table 4. Only two systems
showed a significant output, one turbine 1
system which generated 36.1kWh and one
turbine 2 system which generated 38.0kWh
annually. However, when considering the
inverter energy consumption, all systems
installed at the EcoSmart show village
showed a negative net output, that is, in
every case the inverter consumed more energy
than the wind turbine was able to generate.
All systems experienced considerable down-time
where the turbines were non-operational,
ranging between 10 and 66 days.
4 Discussion
When compared to theoretical energy gener-ation,
the values for measured energy gener-ation
seem very disappointing. While only
two turbines were able to generate a signifi-cant
amount of energy, none of the five
turbines were able to generate a positive net
output including inverter energy consump-tion.
The underperformance of the micro
wind turbine systems in general can be largely
attributed to two factors; turbulence in urban
environment and the use of inverters. In some
cases, such as for turbine 2c, there can be
additional effects from a blocked wind flow
path, in this case from the western direction.
With reference to Table 6 and Figure 1,
turbine 2c has a significantly lower output
than turbine 2b, which does not suffer from a
blocked wind flow path. However, in com-parison
to the other turbines the lack of
performance of turbine 2c does not stand out.
4.1 Inability to deal with turbulence
One main problem that was observed
during the operation of all wind turbines is
their inability to adequately deal with
turbulence.
Table 5 Inverter energy consumption on days with extremely
low wind speeds
Daily Inverter consumption (Wh)
System 08/07/2007 19/07/2007 21/07/2007 Average
Turbine 1a 169.5 170.5 170.0 170.0
Turbine 1b 181.0 180.5 180.5 180.7
Turbine 2a 159.5 159.5 159.5 159.5
Turbine 2b 119.5 120.5 118.0 119.3
Turbine 2c 122.5 130.0 130.0 127.5
Table 6 Summary of wind turbine performance over 12 months
System Energy
generation
(kWh)
Inverter
consumption
(kWh)
Net
output
(kWh)
Downtime
Turbine 1a 36.1 55.9 19.8 36 days
Turbine 1b 3.9 64.1 60.2 10 days
Turbine 2a 1.8 47.7 45.9 66 days
Turbine 2b 38.0 41.5 3.5 18 days
Turbine 2c 5.6 41.1 35.5 43 days

Turbulence is inevitably created around the
sharp edges of the roof of the buildings. This
disrupts the smooth airflow over the turbine
blades by causing sharp and frequent changes
in both wind speed and wind direction. The
turning mechanism of the wind turbines is
relatively sensitive with a large wind vane,
causing the turbines to react promptly to a
change in wind direction. However, while the
turbine changes its position, the airflow is
disrupted even further for some time, causing
the blades to slow down dramatically even
when there is an adequate amount of wind.
Further analysis was conducted using the
weather data that was recorded during the
trial period. The weather data was recorded at
10-min intervals. However, the logging soft-ware
of the weather station received samples
every second and then averaged the values
over the recording interval. While the interval
of 10 min is deemed to be accurate enough for
wind data (BS EN 61400-21:2002), it is
slightly too long for measuring turbulence.
Other research21 confirms that the sampling
period has a significant effect on measured
wind speed frequencies. The 10-min interval
limits the accuracy of the assessment, but will
still provide valuable trends and indications.
The standard deviation of wind data can be
used as a measure for turbulence, where
turbulence must be considered in three
planes, longitudinal, lateral and vertical.12
To find longitudinal turbulence, the hourly
standard deviation of measured wind speed
was found. An indication for lateral turbu-lence
was gained by analysing measured wind
direction. Wind direction was measured in
degrees, ranging from 08 to 3608. Difference
in wind direction was assessed by subtraction,
but when the wind direction went from say
3508 to 108, an algorithm was written to give
this as a difference of 20 as opposed to the
unlikely value of 340. The algorithm pre-vented
changes of greater than 180 which was
considered unlikely over the period of 10 min
sampling time. The standard deviation of
differences in wind direction was calculated
for every hour using the 10-min interval
readings. No measurements were available
for vertical wind speed.
For micro wind turbine performance, it is
expected that lateral turbulence will have the
greatest effect. Both longitudinal and vertical
turbulence will effectively cause a variation of
the wind angle of incidence on the turbine
blades, resulting in less efficient aerodynam-ics,
hence reduced turbine efficiencies. Lateral
turbulence on the other hand will cause the
entire turbine to turn, attempting to redirect
into the changed wind direction. It has been
observed that turbine blades almost come to a
standstill while the turbine redirects itself, so
that during lateral turbulence very little
energy generation is possible.
Turbines 1a and 2b have shown some
energy output, so these turbines are used to
identify the effect of turbulence. Interestingly,
both turbines showed very similar generation
patterns. Between April and June 2007 six
time periods where identified independently
where noticeable energy generation was mea-sured.
Five out of the six generating periods,
ranging from 12 h to approximately 2 days
each, coincided exactly. The differences
between theoretical and actual AC generation
over these periods are shown in Table 7.
Average measured inverter consumption
shown in Table 5 is used for calculations.
Having established that turbines 1a and 2b
tend to generate energy at the same time, and
having pin-pointed these times, the energy
generation can now be correlated to turbu-lence
estimates based on weather data.
When looking at Figures 7 and 8, showing
lateral and longitudinal turbulence through-out
the coinciding generating periods of
turbines 1a and 2b, it becomes apparent that
lateral turbulence has a greater effect on
energy generation. Lateral turbulence was
measured to be noticeably lower during
periods of energy generation compared to
periods where no generation was measured.

Table 7 Turbine generation and inverter losses during coinciding generating periods
Turbine Theoretical
1 28 1a 0.761 0.511 0.250 0.214 86
2b 1.475 0.571 0.904 0.749 83
2 59 1a 1.591 1.368 0.223 0.215 96
2b 2.631 1.503 1.128 0.431 38
3 42 1a 2.973 1.309 1.664 1.058 64
2b 6.132 1.446 4.687 2.224 47
4 13 1a 0.932 0.325 0.607 0.248 41
2b 2.112 0.352 1.760 1.296 74
5 38 1a 2.051 0.892 1.159 0.673 58
2b 4.586 0.989 3.597 2.492 69
1
DC (kWh)
Total inverter
losses (kWh)
Theoretical
AC (kWh)
Measured
AC (kWh)
2 3 4 5
Case Duration
0
23/04/07 01/05/07 07/05/07 14/05/07 21/05/07
(hours)
% of
theoretical
Figure 7 Hourly standard deviation of wind direction differences (lateral turbulence) during generating times of both turbines over a
4-week sample period in 2007
1
120
100
80
60
40
20
23/04/07 01/05/07 07/05/07 14/05/07 21/05/07
Standard deviation
Standard deviation
2 3 4 5
2.5
2
1.5
1
0.5
0
Figure 8 Hourly standard deviation of wind speed (longitudinal turbulence) during generating times of both turbines over a 4-week
sample period in 2007

Periods where no generation was measured
but lateral turbulence was low generally
coincided with periods of low wind speeds.
Figure 8 on the other hand does not appear
to show any direct correlation between low
longitudinal turbulence and turbine genera-tion.
The average measured standard devia-tion
of wind speed at a height of
approximately 10m above the ground is
0.49 m/s. Turbulence intensity, which is ratio
of standard deviation to the mean value22 has
also been calculated for the entire data set. The
longitudinal turbulence intensity over 12
months was found to be 0.52. For comparison,
the longitudinal turbulence intensity in rural,
unsheltered areas at a height of 10m above the
ground is around 0.18.23
While the average standard deviation of
non-generating periods is 0.44 m/s, the aver-age
standard deviation during generating
periods is actually higher, at 0.59 m/s. It can
be deduced that longitudinal turbulence,
meaning frequent changes in wind speed,
does not appear to show a significant
impact on turbine performance.
To investigate this further, Figures 9 and
10 show plots of lateral turbulence for periods
of energy generation (cases 1 to 5 combined),
and periods of no generation (combined
‘gaps’ between cases 1 and 5) respectively.
For this analysis all wind speeds below the
cut-in speed of the turbine 2 (2.5 m/s) have
been neglected to avoid any skewing of the
data, as the wind was found to change
naturally more frequently at lower wind
speeds. The mean of the standard deviations
of wind direction during periods of energy
generation is 6.188.18, while the mean of
the standard deviations of wind direction of
speeds above 2.5 m/s during non-generating
periods is 10.9811.58. To compare these
values, the t-test is used. This statistical
function is able to compare two means in
relation to the variation by finding the stan-dard
deviation of the difference. The expres-sion
used to calculate the t-value (t) is given in
Equation (6)24:
t ¼
x1 x2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
SD2
r ð6Þ
1
n1
þ
SD2
2
n2
For the two datasets shown in Figures 9
and 10 the t-value is 5.36. This is a very high
t-value, meaning that the difference between
the two means is statistically very significant,
well beyond a 0.0001 significance. In relation
to the wind turbines that were tested this
indicates that energy generation is highly
120
100
60
40
0
1 63 125 187 249 311 373 435 497 559 621 683 745 807 869 931 993 1055 1117 1179 1241 1303
No. of samples
20
Standard deviation
80
Figure 9 Combined hourly standard deviation of wind direction differences (lateral turbulence) for periods of energy generation
only, over a 4-week sample period in 2007

dependent on the amount of lateral
turbulence.
The turbulence experienced at the location
of the turbines may even be enhanced by their
mounting position relative to the building,
and by the building geometry that surrounds
them. The weather station is mounted in a
similar position as turbines 1a and 1b, hence
this analysis is very representative for those
two cases. For all turbines of type 2, however,
the turbulence profile will be very different.
Nonetheless it can still be expected to have a
similarly significant effect.
If micro wind turbines are to become a
viable option for the urban environment in
the future, the technology must be improved
dramatically to overcome this problem.
Vertical axis turbines25 could provide a
potential solution if their efficiency can be
improved. It may be possible to reduce the
effect of turbulence by installing a fixed
turbine, which faces into the direction of
prevailing wind, or by channelling the airflow
into the turbine, using building geometry or
otherwise.
4.2 Inverter inefficiencies
The inverter of the wind turbine is another
source of inefficiency. As explained earlier,
inverters require a certain amount of power
for control circuits as well as internal relay
switching. In this case, the power is taken
from the AC side of the inverter, that is, the
power grid. In addition to this, inverter
efficiencies generally drop off under partial
inverter loads.26
Figure 11 shows that the efficiency drops
off dramatically when the inverter load is
below 10%. For the case of the wind turbines,
this would include steady wind speeds below
5 m/s, assuming that the inverter has a rated
power equivalent to the wind turbines.
During the 1-year period, 85% of all wind
speeds that were measured at the EcoSmart
show village were below 5 m/s.
Finding the appropriate size of an inverter
which is to be used in conjunction with wind
turbines can be difficult, as the turbines have
large power output range, and the frequency
of low power generation is much higher than
the frequency of high power generation. One
possible way of avoiding this problem would
be to use an onsite DC battery storage system
instead of converting the electricity to an AC
output. If AC output is required, then the use
of two linked inverters with different power
ratings might be considered, where one
inverter is used for small loads and the
second one comes during periods of high
loads, that is, high wind speeds. The downside
120
100
60
40
0
1 129 257 385 513 641 769 897 1025 1153 1281 1409 1537 1665 1793 1921 2049 2177 2305 2433 256112689
No. of samples
20
Standard deviation
80
Figure 10 Combined hourly standard deviation of wind direction differences (lateral turbulence) for periods of zero energy
generation only, over a 4-week sample period in 2007

to this approach is the increased capital cost
and additional power consumption for a
second inverter control circuit.
4.3 Reliability
System reliability is another important
cause for concern. Table 6 has already pro-vided
an overview of system downtime, which
was found to range between 3% and 18%
over the 1-year period. These downtimes were
largely caused by wind speeds that exceeded
the rated ‘cut-off’ speeds of the turbines.
While average wind speeds over 10 min that
exceed the cut-off speed were rarely mea-sured,
gust speeds may well have been higher
on several occasions. It is also possible that
the power curve supplied by the manufacturer
is not 100% accurate, as was found in other
research projects such as the Warwick wind
trials.8 The excessive wind speeds caused the
turbines to shut down and, on many occa-sions,
they failed to automatically start up
again. The turbines had to be reset manually.
A major issue for concern is the systems’
apparent lack of ability to deal with extreme
wind speeds that far exceed the rated cut-off
speeds. On one occasion during extreme
winds in January 2007, where average wind
speeds above 18 m/s were measured, a blade
from one of the turbines detached and was
later recovered far from the installation site.
Figure 12 shows a photograph of the blade as
it was recovered.
While this is unlikely to have occurred
during standard operation, it is possible that
the shut-down mechanism was responsible for
this. When the wind speeds exceed the rated
cut-off speeds of the turbine, a braking
mechanism is applied, which forces the
blades to come to a standstill. However, at
very high wind speeds the transient loads
during turbine shut-down and the aerody-namic
forces acting on a stationary blade
apply an extreme amount of stress to the root
of the blades. As blades are designed to be
light and thin to maximise turbine efficiency,
safety margins in blade design may not have
anticipated and accounted for the forces
under such extreme wind speeds as experi-enced
on the 18 January 2007.
100
80
60
40
20
0
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
Ppv /Pinv,rated
Inverter efficiency (%)
Figure 11 Efficiency variation with power load of typical inverter14

Figure 12 Damaged turbine blade which had detached during the storm on 18 January 2007
4.4 Payback rate and carbon savings
Feed-in Tariffs for electricity generating
renewable energy systems, such as wind tur-bines,
will be introduced in April 2010. The
FITs are fixed rates at which energy generated
by renewable energy systems will be valued,
and do not require electricity to be ‘exported’
to the national grid. However, if electricity is
exported, an additional 3 p/kWh will be paid
on top of the nominal tariff. The model of the
FIT’s is such that initial tariffs are available
for installations commissioned in or after
April 2010. For wind turbines, the tariff will
be paid over a 20-year period. For any
installation commissioned after April 2012,
there will be a ‘degression’ value for the
initial tariff, which is a reduction of the
tariff to be received over the entire period.
The degression is linked to inflation, and
Table 8 provides an extract6 of tariffs for
wind turbine systems until April 2018.
As none of the micro wind turbine systems
were able to generate a positive electrical
energy output, the actual payback periods
and carbon offset were not calculated.
Instead, the theoretical values will be consid-ered
for this system. Using the theoretical
output from manual calculations and the
Windographer estimates, the payback rates
and carbon offset for the two systems are
shown in Table 9.
For the purpose of these calculations the
following has been assumed:
– The systems are used with either a DC
battery and charge controller, eliminating
Table 8 2010 Feed-in tariffs until April 2018 for wind turbines
Size Annual tariff (pence/kWh), starting in
April 2010 April 2011 April 2012 April 2013 April 2014 April 2015 April 2016 April 2017
Wind 1.5kW 34.5 34.5 32.6 30.8 29.1 27.5 26 24.6
Wind 41.5–15kW 26.7 26.7 25.5 24.3 23.2 22.2 21.2 20.2
Wind 415–100kW 24.1 24.1 23 21.9 20.9 20 19.1 18.2
Wind 4100–500kW 18.8 18.8 18.8 18.8 18.8 18.8 18.8 18.8
Wind 4500 kW–1.5MW 9.4 9.4 9.4 9.4 9.4 9.4 9.4 9.4
Wind 41.5MW–5MW 4.5 4.5 4.5 4.5 4.5 4.5 4.5 4.5

the need for an inverter, or a perfectly
efficient inverter that consumes no energy.
– All of the energy that is generated is
consumed on-site.
– 2010 feed-in tariffs are applicable for these
installations, providing a maximum possi-ble
contribution to annual savings.
– 1kWh of electricity taken from power grid
equates to approximately 1 kg CO2 of
carbon.27
It must be emphasised that this is a ‘best-case’
scenario of purely theoretical nature,
and not a realistic representation of what was
found during the 12-month experimental
period. All systems from the experimental
set-up consistently showed negative annual
electricity generation, hence a negative carbon
offset. Any potential annual savings and
related payback periods would depend on
what the system actually generates, and are
greatly improved by Feed-in Tariffs.
5 Conclusion
– Theoretical wind generation for this partic-ular
site is around 203kWh and 100kWh
annually for turbine 1 and turbine 2 respec-tively,
while measured generation was
below 40kWh for both turbines.
– Inverters consume a considerable amount
of energy, measured to be between 41kWh
and 64kWh annually.
– Considering inverter consumption, the net
energy output of all systems was measured
to be negative, that is, in all cases more
energy was consumed than generated.
– The use of an inverter inevitably causes high
efficiency losses for generation at low wind
speeds.
– Turbulence, in particular lateral turbulence
brought about by changes in wind direction,
is a major concern for the performance of
micro wind turbines in an urban environ-ment.
This needs to be overcome by
improved technology or building
integration.
– At present, micro wind turbines can pose
severe safety problems by detaching blades
during extreme wind speeds.
– During the experiment it was found that all
five systems showed an overall negative
carbon offset over a 12-month period.
– During the experiment no financial savings
were achieved as net generation was nega-tive.
In theory, if the turbines were to work
at design efficiency and without the
observed lateral turbulence and a perfectly
efficient inverter was used, optimistic pay-back
periods for the turbine 1 model are 17–
18 years, and 51–55 years for turbine 2
models assuming 2010 FITs.
Acknowledgments
The work described in this article was supported
by a grant from Barratt Development PLC, who
also provided equipment and testing facilities, and
an EngD grant from the Engineering and Physical
Sciences Research Council. Thanks are given to
Dr Tony Sung who initiated and supervised the
project for 2 years. Thanks are also given to
Dr Alan Williamson Senior Research Fellow at
the University of Manchester for advice on the
inverter theory and to Emeritus Professor Patrick
Laycott University of Manchester, who advised on
aspects of the statistical data analysis.
Table 9 theoretical simple payback rate and carbon offset of
micro wind turbine systems
Turbine 1 Turbine 2
Theoretical generation (kWh) 203 100
April 2010 to March 2012 FIT £70.40 £34.50
Annual savings £20.30 £10.00
Theoretical payback rate (yrs) 16.5–18.1 50.6–55.1
Carbon offset (kgCO2) 203 100

References
1 DTI Energy Group. Our energy future –
creating a low-carbon community. UK
Government Energy White paper, 2003.
2 DTI Energy Group. UK energy end users,
2004.
3 Department of Communities and Local
Government. Code for sustainable homes,
2007. Available at: http://www.planning
portal.gov.uk/uploads/code_for_sust_
homes.pdf (accessed January 2010).
4 Department of Communities and Local
Government. What standards may be in 2013
2016, 2007. Available at: http://www.
communities.gov.uk/documents/planningand
building/doc/br-energyefficiency.doc (accessed
January 2010).
5 Clausen PD, Wood DH. Recent advances in
small wind turbine technology small wind
turbines. Wind Engineering 2000; 24(3):
189–201.
6 Eliasson I, Offerle B, Grimmond CSB,
Lindqvist S. Wind fields and turbulence
statistics in an urban street canyon.
Atmospheric Environment 2006; 40: 1–16.
7 Kastner-Klein P, Fedorovich E, Rotach MW.
A wind tunnel study of organised and turbu-lent
air motions in urban street canyons.
Journal of Wind Engineering and Industrial
Aerodynamics 2001; 89: 849–861.
8 Encraft Warwick Wind Trials Final Report.
Available at: http://www.warwickwindtrials.
org.uk (accessed 8 July 2010).
9 Cace´ J, Horst E, Syngellakis K, Niel M,
Clement P, Heppener R, Peirano E. Urban
wind turbines: Guidelines for small wind
turbines in the built environment. Available at:
www.urbanwind.org (accessed 8 July 2010).
10 Heath MA, Walshe JD, Watson SJ.
Estimating the potential yield of small
building-mounted wind turbines. Wind Energy
2007; 10(3): 271–287.
11 Celik AN, Muneer T, Clarke P. An investiga-tion
into micro wind energy systems for
their utilization in urban areas and their life
cycle assessment. Proceedings of the
Institution of Mechanical Engineers Part A:
Journal of Power and Energy 2007; 221(8):
1107–1117.
12 Bahaj AS, Myers L, James PAB. Urban energy
generation: Influence of micro-wind turbine
output on electricity consumption in buildings.
Energy and Buildings 2007; 39(2): 154–165.
13 Office of Public Sector Information. Planning
and Energy Act 2008. Available at: http://
www.opsi.gov.uk/acts/acts2008/pdf/ukpga_
20080021_en.pdf (accessed February 2010).
14 Department of Energy Climate Change.
Feed-in Tariffs – Government’s response to
2009 consultation. Available at: http://
www.decc.gov.uk/Media/viewfile.ashx?
FilePath¼ConsultationsRenewable%20
Electricity%20Financial%20Incentives1_
20100204120204_e_@@_FITsconsultation
responseandGovdecisions.pdffiletype¼4
(accessed January 2010).
15 Wood DH. A blade element estimation of the
cut-in wind speed of a small turbine. Wind
Engineering 2001; 25: 249–255.
16 Wood DH. Dual purpose design of small wind
turbine blades. Wind Engineering 2004; 28(5):
511–527.
17 Mistaya Engineering Inc., Windographer.
Availabale at: http://www.mistaya.ca.
18 Brutsaert W. Moist air. Evaporation into the
atmosphere: Theory, history and applications.
Dordrecht, Holland: D. Reidel Publishing
Company, Chapter 3.1, pp. 37–42, 1991.
19 Ishikawa T (2002). Grid-connected photovol-taic
power systems: survey of inverters and
related protection equipments. Report
IEA-PVPS T5-05, Central Research Institute
of Electric Power Industry Japan.
20 Grauers A (1994). Synchronous generator
and frequency converter in wind turbine
applications: system design and efficiency.
Technical Report no. 175 L, ISBN
91-7032-968-0, Chalmers University
of Technology.
21 Makkawi A, Celik AN, Muneer T. Evaluation
of micro-wind turbine aerodynamics,
wind speed sampling interval and its spa-tial
variation. Building Services
Engineering Research and Technology 2009;
30(1): 7–14.
22 Roth M. Review of atmospheric turbulence
over cities. Quarterly Journal of the Royal
Meteorological Society 2000; 126(564):
941–990.

23 Holmes JD. Wind loading of structures.
London: Spon Press, Chapter 3, pp. 46–68,
2001.
24 Gravetter FJ, Wallnau LB. Essentials of sta-tistics
of the behavioural sciences. Belmont CA:
Thomson Wadsworth, Chapter 10,
pp. 266–267, 2008.
25 Revolutionary wind turbine. Building Services
Journal, CIBSE, June 2007.
26 Mondol J. Sizing of grid-connected photovoltaic
systems: SPIE (International Society for Optical
Engineering). 10.1117/2.1200704.0612, 2007.
27 Parliamentary Office of Science and
Technology. Carbon footprint of electricity
generation - postnote, October 2006. Available
at: http://www.parliament.uk/documents/
upload/postpn268.pdf (accessed November
2009).

Micro wind turbine performance in urban environments

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Micro wind turbine performance in urban environments

Similar to Micro wind turbine performance in urban environments (20)

Micro wind turbine performance in urban environments