turbine prices tend to decline they still require a large
investment, so before deciding to install one it makes sense to measure
the wind speed at the proposed site to make
sure it is suitable. Knowledge of the typical wind behaviour also helps
with specifying the type of wind turbine to use and the support
equipment needed, such as an inverter.
Wind speed is usually measured with an anemometer which
records the average velocity experienced over regular intervals; a
of 10 minutes being a popular choice. Once sufficient data has been
collected it needs
to be analysed.
Turning a file of raw data from an anemometer into a wind
distribution can be a challenge so I have
prepared an example spreadsheet to help. The Open Office version is at;
This has been converted to Excel format at;
Instructions to get you started are included on the first page (or sheet).
The spreadsheet calculates average wind speed, root mean cubed
(RMC) wind speed and the Weibull speed parameter. If the shape
parameter in cell I3 is set to 2 then the more general Weibull equation
Rayleigh equation. This is a popular way for
wind turbine sites.
The second sheet demonstrates how to take the measured
distribution and use it to predict the energy produced by some example
The first step is to paste the raw wind speed data into
column B on the first sheet [Stats]. Column A can take the date and
time data if you want to include that, but it is not required. The
example already in the sheet comes from a site I was asked to assess
recently. We did not recommend putting a turbine there! Hopefully you
will have better data to paste in but the example is used because it is
not atypical of sites actually chosen and it highlights why turbines on
those sites perform so poorly. The large number of zero readings are
due to friction in the anemometer bearings preventing it turning in
very light winds.
the raw speed data is pasted in, the sheet automatically
calculates the average speed, and also the root mean cube speed (RMC)
which is another comparison figure sometimes used. However, a Weibull
distribution is an even better system for assessing wind resources. The
parameters for the equation could be calculated using a suitable
minimization of errors method but with a modern computer it can be done
well enough by trial and error. The wind changes every year so it is
pointless trying to get high accuracy. Start with a shape parameter
I3) of 2 because most places are reasonably well modelled by this
distribution (called Rayleigh). Adjust the speed parameter in cell I5
until the value in cell I1 is close to 100. This represents the value
when the wind energy from the modelled distribution is the same as the
energy from the raw data.
Watching the shape of the two curves on the graph helps with
and error process. The blue line is the raw data and the orange line is
the modelled data. If the lines are not roughly the same shape then the
parameter can be changed to correct for this. The speed parameter
obviously needs resetting after each change of shape parameter.
Once the wind speed distribution has been modelled, the second
sheet [Energy prediction] can be used to estimate the energy output
from a particular
turbine. In Cell C7 enter the height of the turbine's hub above sea
meters) and in D7 enter the average temperature at hub height
(centigrade). These cells are used to calculate the density of the air
passing the turbine. In Cell K4 enter the height of the anemometer
above ground level and in Cell L4 do the same for the hub. Cell M4 is
for the roughness factor (or Hellman exponent or friction coefficient)
which is used to model the rate at which the
wind speed increases with height above the ground. There are a number
of examples given starting in cell P4.
Many of the cells requiring user input have a red dot in the
top right corner. This means that by hovering the mouse over this cell
a comment with more explanation appears.
Three turbine examples have been chosen ranging from a
micro-turbine used for keeping batteries charged to one suitable for
very energy efficient dwelling to a large one capable of running a
whole village or small town. The figures are not guaranteed to be
accurate, but they
illustrate how real turbines only achieve high power coefficients over
range of wind speeds.
describing particular turbines it is most common to see power curves
published but if power coefficients are used instead it is possbile to
correct for altitude and temperature. These are not normally large
corrections, and things like wind turbulence and long-term seasonal
have a bigger impact, but it does improve the accuracy of the energy
The last of the 4 columns devoted to each turbine example
calculates the power generated at a selection of wind speeds. If you
only have a power curve you can determine the power coefficient at each
wind speed by adjusting them until the estimated power matches the
measured power. Most power curves are reported for sea level at 15°C so
adjust cells C7 and D7 to 0 and 15 first.
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