DEPLOYMENT OF A NETWORK OF AUTOMATIC WIND-MEASUREMENT STATIONS IN SOUTH
PATAGONIA
(1)
Ing. Rafael Oliva1, Ing. Carlos Albornoz2
Universidad Nacional de la Patagonia Austral - Energías Alternativas
(2)
Servicios Públicos Sociedad del Estado
Lisandro de la Torre 1070
Te/Fax +54 (0) 2966 442317/430923
9400 Río Gallegos - Argentina
E-mail: micro-en@unpa.edu.ar
ABSTRACT: This work describes the evolution and deployment of a network of automatic measurement stations for the
assessment of wind speed and direction in a number of sites within Santa Cruz, a province of Argentina located at the
south tip of continental Patagonia. The network, which is property of the utility Servicios Públicos Sociedad del Estado
(SPSE) and is logistically supported by the Universidad Nacional de la Patagonia Austral (UNPA), was officially
established in late 1997 for joint evaluation of wind resources near major towns and communities. It consists of
measurements taken mostly at a single 10m height, (some are taken additionally at 20 or 30m) using NRG and BAPT
Wind Loggers. Data collection is performed on a bi-monthly basis by specialized personnel using cartridge swapping
and notebook PCs. Windows-based Software was developed at UNPA to automate the processing of the older BAPT
Loggers, and to coordinate their outputs with the more modern and reliable NRG Loggers. A description of the problems
encountered, software issues and first results obtained is included in the work. It is expected that the data, the special
characteristics and the harsh climatic conditions under which the measurements are taken will deem the issues discussed
useful for other groups involved in similar activities.
Keywords: Wind Speed, Data Acquisition Systems (DAS), Prediction
1 INTRODUCTION
Measurement of the wind resource in the Southern
Patagonia area, particularly within the regions of Santa
Cruz and Chubut in Argentina, and Magallanes in Chile,
is becoming increasingly important. There are also
numerous isolated communities with their energy
requirements only partially fulfilled, which could benefit
from the extended use of renewable energy resources
such as wind power. From a total demand of 205GWh
(1998 values for Santa Cruz) managed by SPSE, only
30% (62GWh) are from systems linked to the Patagonic
Grid (PG), in the north of the province, the rest being
gas or diesel-fired thermal generation systems in isolated
grids. An additional 8.5GWh are demanded by Pico
Truncado, where the electrical supply is managed by the
Municipal authority, also connected to the PG but with
around 30-40% of demand supplied by a 1.2MW
Enercon/Wobben wind park installed in 2001.
Data analysis from an existing Regional Wind Atlas
[1] has been available as a reference work, clearly
oriented to use with wind-energy applications. Still, since
the basic data for this work was taken from National
Meteorological Service (SMN) airport information with
non-automatic logging, the authors considered that
greater detail and more recent data would be significant
contributions to the development of local wind-power
generation.
To address these issues, a network of automatic wind
stations was proposed in 1997 as a joint project between
the state-owned Utility SPSE and the University of South
Patagonia (UNPA). Data from the stations was to be
prepared for wind-energy applications, most of which are
expected to be isolated low and medium power grids.
The stations were not related to individual wind-turbine
installation projects, but they would be installed in
favourable sites, in the outskirts of towns and in most
cases with individual fencing and protection to assure
reliability of the data. More detailed siting analysis and
installation of sensors at hub height will be necessary
once commercial projects are in a more defined stage.
However, the data produced can be of great value as
reference wind conditions, for the installation of small
wind turbine systems or for validation of future windmaps generated with remote-sensing techniques [2][3].
2 APPLIED THEORY
2.1 Statistical description of wind energy
The most usual measurement of wind intensity is the
annual average at a given site, usually noted as <V>
[m/s]. The definition of this average, taken for the
random variable v, is:
∞
∫
< V >= vf (v)dv
(I)
0
Frequently the probability distribution function f(v)
is not known for the measurement site, and it is necessary
to find an approximate expression using a time series of
values. The time series is later processed and condensed
to produce an histogram, from which the theoretical f(v)
can be fitted. The histogram can also be obtained from
an electronic classifier, which has several bins or
divisions,
for example, at 1 m/s intervals. The
occurrence of different levels of wind results in the
increment of the corresponding bin counters. The results
can be graphed in a histogram, producing an
experimental (discrete) wind probability distribution.
Both methods are available or can be derived from data
using modern wind loggers.
Experience has shown [4] that real-world wind
distributions can be fitted well to a Weibull-II
distribution, which can be written as:
kv
f w (v ) =  
A A
k
k −1 −  v 
A
 
e
(II)
Strictly speaking, the physical meaning of f w (v)dv is
the probability of windspeed measuring between v and
v+dv. The Weibull distribution has two parameters,
usually named scale factor A, [m/s] and form factor k [].
These parameters can be calculated from the data, and
later used for description of the energy available at the
site. A useful expression is the Wind Speed Duration
Curve, in hours per year,
  V K 
 −  a  
  A  
(III - IV)
H (V ≥ Va ) = 8760 e
which is a measure of the number of hours of windspeed
greater than a value of Va in a year.
To calculate the average <V> [m/s] with fw(v) we
can substitute as follows:
∞
k
k −1 −  v 

 A
 k  v
< V >= v  
 A  A 
0
∫
and use the variable z =
( Av )k
e
(V)
to write:
k
or else
F (vi ) = Pi = 1 − e
1 − Pi = e
and taking natural logarithms twice,
ln(−(ln(1 − P )) = k ln v - k ln A
i
i
The variable change:
ln(−(ln(1 − Pi )) = yi
ln vi = xi
∫
(VI)
0
where the usual Gamma function has been used.
Since A,k parameters are calculated from data at a
specific site and height, and noticeable variations can be
observed when these conditions vary, it is possible to
extrapolate with caution for different heights. In absence
of better solutions, expressions for the variation of
Weibull parameters with height as proposed in [5] can be
used, and their conversion to metric units gives:

 href


1 − 0.088 ln


 3.046667 
(VIII)
k = kref 

h



1 − 0.088 ln
 3.046667 

[0.37 − 0.088 ln(2.236842105 A )]
ref
(IX)
n
 h 

(X)
A = Aref 
 href 


where kref, Aref
designate values calculated from
reference data at height href, and A,k are the extrapolated
values for the height h. Both Aref,A, are in m/s, h in m,
and k is adimensional.
The Rayleigh distribution function is a special case of
the Weibull distribution, for which k=2, producing an
average:
π
[m/s ] (XI)
2
This distribution is adequate for describing wind in low
turbulence sites, with only one parameter. Results using
this distribution are also shown for comparison in this
paper.
< V >= AΓ(1 + 12 ) = A
2.2 Estimating the values of k and A:
A simplified calculation process will be exposed as
suggested by several authors [1,2] from which the
Weibull parameters can be obtained using the data from
v 
−  i 
 A
k
(XII)
(XIII)
(XIV)
y i = bxi + a
to be evaluated
and then, based on the values of coefficients b, a
obtained, it is simple to show that:
−
1
z
< V >=  Az k e − dz = AΓ(1 + k1 )



 href


1 − 0.088 ln

3
.
046667



v 
−  i 
 A
allows a straight line fit
dv
∞
n=
wind loggers. A histogram will be supposed to be
available from the measured data. The histogram consists
of the numbers of occurrences of wind velocities in bins
from 0-V1 , V1 -V2 , etc. with frequencies (counts) f1 , f2,
etc. The method starts by calculating the cumulative
frequencies Pi such that P1 =f1, P2= P1 +f2, ......Pi= Pi1+fi. Recalling from the Weibull distribution as discussed,
it holds that:
a
A = e b [m/s ]
k =b []
(XV)
which are the required coefficients. This estimate must be
made considering a sufficiently long period of time. A
number of software packages ,e.g. ALWIN, WAsP [4]
that can automate the process of calculating the Weibull
parameters from a given time series of measurements. For
our work, these have been used for verification and
control purposes.
2.3 Estimating the Annual Energy Output (AEO).
An approximate value of the energy produced by a
wind turbine with a power curve P(Vi), in a period of
time ∆T, is given by:
k
 − Vi + ∆v  k
 V − ∆v  
− i
  A 
 
 A 
∆T P (Vi ) e
−e
E ∆T =
 (XVI)


i =1


Substituting the period for 8760 hours, the AEO can be
predicted. Vi is the center value of the bin, and 2∆v the
bin width in [m/s]. The values of A,k used need to be
corrected as in 2.1 if the turbine hub height (HH) is
different from the reference or measurement height.
Finally, an AEO using the Rayleigh distribution can be
obtained by simply replacing A with the value given by
(XI), using k=2. Supposing the hub height is different
from the reference value, and that the roughness and
obstacles are negligible, the following expression [4] can
be used for the mean windspeed at hub height.
m
∑
 h
< V > HH =< V > ref 
 href

1/ 7




(XVII)
2.4 Other statistical quantities
The standard deviation is the positive square root of the
variance, which can be evaluated from the following:
∞
σ2 =
∫ (v- < V > )
2
f (v )dv
[m 2 /s 2 ]
(XVII)
0
If a Weibull distribution is used, this can be written as:
σw = A
[(Γ(1 + )− Γ (1 + ))]
2
k
2
1
k
[m/s ]
(XVIII)
For a given site, the average meteorological power per
unit area A can be expressed by:
< Pa > 1
= 2 ρ < V 3 > [W/m 2 ] (XIX)
A
If the Weibull parameters are known, then this average
power can be obtained from:
< Pa > 1
= 2 ρκ e < V >3 [W/m 2 ] (XX)
A
where ke is known as EPF [6] or energy pattern factor,
taking a range of values between 1.5 and 3. The EPF is
calculated in our output tables from:
Γ 1 + 3k
<V3 >
[ ] (XXI)
=
κe =
< V >3 Γ3 1 + 1k
( )
( )
The Gamma function is implemented in Excel so it is not
necessary to use tabulated values in our calculations.
Figure 1: Left: NRG Station at Gobernador Gregores; Center: Lago Posadas NRG; Right: Los Antiguos BAPT
3 NETWORK CONSTRUCTION
3.1 Key issues
The network was initiated with an agreement signed
between SPSE and UNPA in 1997, deploying four BAPT
wind loggers which had been purchased by SPSE in
1993. A further 2-level BAPT unit was purchased in
1998, but reliability and software problems shifted
decision of new purchases towards the NRG loggers.
Five units of NRG 9200+ loggers and three NRG Wind
Explorer units were purchased since 1998, and an
additional Wind Explorer unit for Río Turbio was added
by the University in 2000 (Figure 1).
Approximate siting of these loggers can be seen in
Figure 2. All data is processed in Río Gallegos, where the
utility SPSE and the University have their headquarters.
The most distant station is 1100km away by road, and
some of the stations are inaccessible in winter, for a
period of at least 30-60 days. These issues can result in a
big logistical problem when data retrieving and servicing
of the stations is necessary, and have been accounted to
be the most costly elements (together with the initial
investment in loggers, instruments and towers) of the
Project.
As for averaging period, BAPT stations work with
60minute averages. Although the first year with the NRG
loggers 10min averages were used, later 60min was
found to be more reliable due to limited chip capacity in
winter periods. For periods with 10min data, NRG
software allows exporting the condensed hourly data base
to .XLS (Excel) format, so this was the input used for
post-processing in spreadsheets.
3.2 Software issues
Most of the software for managing the data from the
BAPT stations had to be written in-house by the
Alternative Energy Group at UNPA. These loggers
produce binary files which can be converted by the
supplied DOS program BAPT2 into ASCII or WK1
(older Lotus spreadsheet) files. No data analysis or even
average extraction could be performed with the BAPT2
program, so a Windows-based software called
BAPTReader was written for this project. A typical
output window of this package can be seen in Figure 3.
Figure 2: Measured sites in Santa Cruz and logger used.
This program was written in C++Builder 4, and uses a
commercial library of mathematical routines from Mix
Software (C/Math Toolchest) in source form. For more
information, a visit to the University site
(www.unpa.edu.ar ->Alternative Energy (Spanish and
English) is recommended
software packages, such as Alwin for Windows, and
WAsP (DOS Version). Most results were within 10% of
the calculated values.
3.3 Representative Data
Some of these results are shown in Table I. In all
cases the period analyzed is at least 12 months, and in
most cases the lost hours are minimal. Sites are selected
with no particular wind installations in mind, so micrositing will probably produce better wind averages. The
annual energy output was based on the power curve of an
Enercon E40 (600kW), similar to those installed in the
Pico Truncado wind park, and is computed both for
Weibull (corrected A,k factors for height) and for
Rayleigh distributions at hub height. The goodness of
adjustment coefficient was suggested in [7] and is a
measure of the quality of the A,k pair calculation and
regression fit.
Figure 2: BAPTReader 1.06 Histogram Window.
Data analysis with the NRG stations (which also
produce binary and ASCII output files) was a
considerably easier task with the provided MicroSite
v2.07 software package. Still, such parameters as Weibull
distribution coefficients are not directly obtainable with
MicroSite, and the converted hourly databases had to be
exported and post-processed in Excel to obtain these
coefficients.
With the cooperation of other institutions, the postprocessing in spreadsheet was verified using other
BASIC PARAMETERS
Measurement Height (MH)
Measured Period
Station Type
Averaging Period
Missing Hours
% Missing Hours/Total Hs
<V> Annual Average - M
σ Standard Deviation - M
Average of Cubes - M
κE EPF-M []
k [Weibull] Anual MH
A [Weibull] Anual MH
VALUES ESTIMATED with k,A
<V> Annual Average - E
σ Standard Deviation - E
Average of Cubes - E
κE EPF-E []
(I) ADJUSTMENT COEF. for A,k:
3
3
3
ABS((V E-V M)/V E)*100
Tres Lagos
18.0
01-99/01-00
BAPT-EVD1
60min
120.0
1.37%
8.70
4.78
1325.78
2.01
1.987
9.808
Gob.Gregores
10.0
01-01/01-02
NRG 9200+
60min
30.0
0.34%
6.619
4.685
793.087
2.735
1.400
7.108
4 ACKNOWLEDGEMENTS
To SPSE, UNPA and to Mr. Sergio Albornoz, our
technical support for the network maintenance and
survival.
SITES
San Julián
Los Antiguos Lago Posadas Las Vegas
12.0
30.0
20.0
10.0
03-99/03-00
01-99/01-00
01-01/01-02
01-01/01-02
BAPT-EVD1
BAPT-EVD1
NRG-9200+
NRG W.E.
60min
60min
60min
10min (*)
34.0
1512.0
2.0
0.0
0.39%
17.27%
0.02%
0.00%
7.346
7.456
5.349
5.822
4.011
3.922
4.566
4.140
787.986
785.011
563.190
569.843
1.988
1.894
3.680
2.887
1.915
2.253
1.347
1.491
8.215
8.372
6.108
6.383
Units
[m]
[m/s]
[m/s]
[m3/s3]
[]
[]
[m/s]
8.69
4.57
1262.73
1.92
6.478
4.689
822.732
3.026
7.288
3.962
773.317
1.998
7.415
3.483
697.936
1.712
5.603
4.203
567.371
3.226
5.767
3.938
526.328
2.744
4.99%
3.60%
1.90%
12.48%
0.74%
8.27%
[%]
50.000
50.000
50.000
50.000
[m]
2.23
0.1294
9.88
8.752
2.39
0.1404
8.99
7.972
1.49
0.1677
7.12
6.434
1.77
0.1519
8.15
7.255
[]
[]
[m/s]
[m/s]
2315.58
1961.23
1371.36
1673.14
[MWh/yr]
ENERGY ESTIMATION FOR AN ENERCON E-40 / 600kW WTG
Hub Height (HH)
50.000
50.000
(II) A,k Adjusted for HH (Using Eggleston-Stoddard)
k [Weibull] Annual HH
2.22
1.66
n [auxiliary exponent]
0.1164
0.1414
A [Weibull] Annual HH
11.05
8.92
<V> Annual Average - EW /HH
9.784
7.975
AEOW (Energy Output - Yr / MWh)
2725.84
1947.71
(III) Values with Rayleigh Distribution.
<V> Annual Average - ER/HH
10.06
8.15
AEOR (Energy Output - Yr / MWh)
2754.48
2045.83
8.94
7.98
6.39
7.26
2359.16
1971.50
1261.91
1656.49
[m/s]
[m/s]
[m3/s3]
[]
[m/s]
[MWh/yr]
Table I: Selected Calculations from representative sites in Santa Cruz.
REFERENCES
[1] Barros, Vicente R. (CONICET/CREE) Atlas del
Potencial Eólico del Sur Argentino. CENPAT 1986.
[2] Mattio, H.F., Ponce, G.A. La Importancia del
Estudio del Viento en un Sistema de Conversion de
Energia Eolica, CREE-Argentina 1995
[3] Project FONDEF D01 i1165 Caracterización y
Aprovechamiento Integral de la Energía del Viento en
Chile, by CERE/UMAG - Financed 2002.
[4] Molly, J.P., Windenergie; Theorie,Anwendung,
Messung , Verlag C.F. Müller , Karlsruhe, Germany, 2nd
edition 1990, ISBN 3-7880-7269-5.
[5] David M.Eggleston, Forrest S.Stoddard Wind Turbine
Engineering Design, 1987, Van Nostrand Reinhold Co.,
ISBN 0-442 22195-9 Pg. 68
[6] Jamil, M. Wind Power Statistics and Evaluation of
Wind Energy Density, Wind Engineering, 1995, Vol. 18,
No. 5, pp. 227-240
[7] Marchante M. et al ; Estimating the Weibull
Parameters using two different Methodologies, EWEA
Wind Power for the 21st Century Conference, Kassel,
September 2000.