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Revue des Energies Renouvelables SMEE10 Bou Ismail Tipaza (2010) 143 149

143

A comparative simulation of the performance of five wind turbine in the site of Borj-Cedria, Tunisia

A.W. Dahmouni1*, M. Ben Salah1, F. Askri2, C. Kerkeni1 and S. Ben Nasrallah2

1 Laboratoire dEnergtique et de Procd Thermique, Centre de Recherche et des Technologies de lEnergie, Technopole de Borj-Cedria, BP 95 Hammam Lif 2050, Tunisie.

2 Laboratoire dEtudes des Systmes Thermiques et Energtiques, Ecole Nationale dIngnieurs de Monastir, Avenue Ibn El Jazzar 5019, Monastir, Tunisie.

Abstract - This paper presents a study of the electrical wind energy production in the site of Borj-Cedria. The data collected at 10 m height during 2008 and 2009, have been used to estimate the monthly net energy output of five commercialized 2 MW wind turbines. This comparative simulation shows a great difference in wind generators production and allows us to choose the best wind turbine adapted to the site conditions.

1. INTRODUCTION

In the last century, several climate changes have been observed in regions all over the world. The main cause of these climatic changes is the rise of fossil fuel uses, which is due to the important demographic and industrial development. These negative effects have forced scientists to draw attention to renewable energy sources, which are the more suitable solution in the future.

Recently, many researchers have interested to the estimation of wind resource and Wind turbine production. Carta et al. [1] have developed a bivariate probability model for wind power density and wind turbine energy output estimation. In many sites over the world an experimental estimation of wind resources have been carried out. Among others, we found the work of Ahmed Shata et al. [2] which represents a data bank of electricity generation and wind potential assessment of Hargada in Egypt. An investigation of wind characteristic and wind energy potential in Kirklareli in Turky was conducted deduced by Gokcek et al. [3].

We found also a review of wind energy potential in four typical locations in Ethiopia developed by Bekele et al. [4]. An estimation of the parameters of the Weibull wind speed distribution was deducted by Segero et al. [5].

The Wind energy potential of Gokceada Island was evaluated in the study conducted by Eskin et al. [6] using the wind data collected in four different locations in Island at two altitudes. Gkek et al. [7] have evaluated the electricity generation and the energy cost of eight wind energy conversion systems in many locations at Central Turkey.

Ouammi et al. [8] have studied the wind energy potential in Liguria region in Italy from the data collected by 25 stations distributed over the four provinces. Himri et al. [9] have presented an analysis of data collected between 2002 and 2006 in four selected sites in Algeria as well as preliminary evaluation of the wind energy potential.

In this general context our study is devoted to the simulation of the electrical production of five similar commercialized 2MW wind turbine. Using over 84450

* dahmouni_anouar_wajdi@yahoo.fr

A.W. Dahmouni et al.

144

observations collected in the site of Borj-Cedria during the last two years, we estimate the monthly and the annual net energy production.

2. THEORETICAL MODEL 2.1 Wiebull distribution

Wind velocity distribution can be modelled by several functions. According to Gumbel [10], the best one is Weibull distribution. This function can be described by two or three parameters. The advantages of the use of the function of Weibull with two parameters were highlighted by Justus et al. [11, 12]. A model of Weibull with three parameters was proposed by Van der Auwera et al. [13]. This model is a generalization of the Weibull function with two parameters.

In wind industry, the use of the Weibull function with two parameters is frequent. Which is expressed by:

=

k1k

AVexp.

AV.

Ak)V(f (1)

Where )V(f is the probability density function, c and k are respectively the scale and the shape parameters which can be calculated using {Eqs (2) et (3)}.

1n

1ii

i

ki

n

1ii

ki

n

)V(ln

V

)V(ln.V

k

==

=

(2)

=

=

n

)V(ln.V

c

n

1ii

ki

(3)

Where n is the observation number and iV the wind speed. 2.2 Wind power density

For a series of measurement, the mean wind power density in the site is given by the following expression:

3V21P = (4)

{Eq. (4)} depends on the frequency of each velocity, thats why mean wind power density is given by:

=0

3 Vd)V(fV21P (5)

So, the {Eq. (5)} has the advantage of making it possible to quickly determine the average of annual production of a given wind turbine if we know its characteristics and the Weibull distribution on the site.

For any wind turbine the electrical power output for each wind speed is given by:

SMEE2010: A comparative simulation of the performance of five wind turbine in the

145

3pout V2

1S)V(C)V(P = (6)

Where )V(Cp is the performance coefficient of the wind turbine at the wind speed V ; S is the rotor area of the wind turbine.

{Eqs. (5) and (6)} give the mean electrical power output:

=max

min3

pout Vd)V(fV21S)V(CP

=max

min outoutVd)V(f)V(PP (7)

minV and maxV are respectively the minimum and the maximal wind speed in the site.

2.3 Wind speed extrapolation The wind speed measurements are collected in the site at 10 m above ground level.

For wind turbine simulation, it is necessary to estimate the wind speed at the turbine hub height. According to the literature, the most commonly used method to adjust the wind velocity at one level to another is the power law method expressed by:

=

mesmes h

hVV (8)

Where mesV is the wind speed recorded at anemometer height mesh , V is the wind speed to be determined for the desired height h , is the power law exponent (equal to 0.14 in this study).

3. RESULTS AND DISCUSSION The industry of wind turbine has considerably evolved principally in Germany,

Netherlands, USA, Canada, Spain and France. A large variety of manufactured wind generators provide electric power between 0.25 and 5000 kW. In this part of paper, we have evaluated and compared the seasonal net energy production of five commercialized 2 MW wind turbines supposed to be installed at 80 m above ground level.

The technical data of the five wind machines are summarized in Table 1.

Table 1: Technical characteristics of the studied wind turbines Description Manufacture Rated

Output (kW) Diameter

( m ) AAERA-2000/80 AAER 2000 80

De Wind D8.2 EU Energy plc 2000 80 Ecotecnia 80 2.0 Ecotecnia 2000 80

Gamesa G80- 2.0 MW

Gamesa Eolica 2000 80

Vestas V80 2.0 MW

Vestas Wind System A/S

2000 80

A.W. Dahmouni et al.

146

Figure 1 presents the curves of the studied wind turbines given by each manufacture for an air density equal to 1.225 kg/m3. As shown, the power output of wind turbines quickly increases and takes its maximum value at the nominal wind speed near of 12 m/s. The cut-in wind speed of the studied turbines is about 3.5 m/s. However, the cut-out wind speed does not exceed 25 m/s.

Fig. 1: Power curves of studied wind turbines

To estimate the energy output of each wind turbine, a procedure was developed based in the wind distribution function in the site and the data provided by each manufactures.

Figures 2 to 6 represent the monthly net energy output for the five wind turbines supposed to be installed at 80m above ground level. We note a difference between the wind turbines production. The highest monthly energy productions have been observed in March and May. However the lowest values are observed in the month of October and September.

Fig. 2: Monthly net energy output for the AAER A-2000/80 wind turbine

Fig. 3: Monthly net energy output for the DeWind D8.2 wind turbine

SMEE2010: A comparative simulation of the performance of five wind turbine in the

147

Fig. 4: Monthly net energy output for the Ecotecnia 80 2.0 wind turbine

Fig. 5: Monthly net energy output for the Gamesa G80-2.0 wind turbine

Fig. 6: Monthly net energy output for the Vestas V80 2.0 MW wind turbine

As can be seen in the figure 7, the wind turbine AAER A-2000/80 presents the highest annual production with a value of 4.703.793 kWh/year.

The wind turbine Gamesa G80-2.0 MW seems to have a good efficiency and confirm the study conducted by the Tunisian company of electricity and gas (STEG) in many locations in Tunisia.

The DeWind D8.2 wind turbine presents the minimum energy production equal to 3.730.558 kWh/year.

A.W. Dahmouni et al.

148

Fig. 7: Annual net energy output for the studied wind turbines

As a result, we can classify the studied wind turbines in three categories. The first group includes the wind turbine AAER A-2000/80 and Gamesa G80-2.0 MW, which have the best annual production. The second category comprises the wind turbine Vestas V80 - 2.0 MW and Ecotecnia 80 2.0, with a good efficiency. The wind turbine DeWind D8.2 represent the third category with the least energy output.

4. CONCLUSION In this study, the electrical generation of five similar commercialized wind turbines

was discussed. Using the data collected in the site of Borj-Cedria and the data provided by each manufactures, the wind turbines have been classified in three categories.

Results show that the AAER A-2000/80 is the best aerogenerator adapted to the site condition in Borj-Cedria. The annual energy output obtained prove that the site of Borj-Cedria presents a promising wind potential and confirms the importance of choosing the suitable commercialized wind turbine for the implantation site.

REFERENCES [1] J.A. Carta and D. Mentado, A Continuous Bivariate Model for Wind Power Density and Wind

Turbine Energy Output Estimations, Journal of Energy Conversion and Management, Vol. 48, N2, pp. 420 - 432, 2007.

[2] A.S. Ahmed Shata and R. Hanitsch, Electricity Generation and Wind Potential Assessment at Hurghada, Egypt, Renewable Energy, Vol. 33, N1, pp. 141 - 148, 2008.

[3] M. Gokcek, A. Bayulken and S. Bekdemir, Investigation of Wind Characteristics and Wind Energy Potential in Kirklareli, Turkey, Renewable Energy, Vol. 32, N10, pp. 1739 - 1752, 2007.

[4] G. Bekele and B. Palm, Wind Energy Potential Assessment at Four Typical Locations in Ethiopia, Applied Energy, Vol. 86, N3, pp. 388 - 396, 2009

[5] J.V. Seguro and T.W. Lambert, Modern Estimation of the Parameters of the Weibull Wind Speed Distribution for Wind Energy Analysis, Journal of Wind Engineering and Industrial Aerodynamics, Vol. 85, N1, pp. 75 - 84, 2000.

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[6] N. Eskin, H. Artar and S. Tolun, Wind Energy Potential of Gokceada Island in Turkey, Renewable and Sustainable Energy Reviews, Vol. 12, N3, pp. 839 - 851, 2008.

[7] M. Gkek, M. Serdar Gen, Evaluation of Electricity Generation And Energy Cost of Wind Energy Conversion Systems (Wecss) in Central Turkey, Applied Energy, Vol. 86, N12, pp. 2731 - 2739, 2009.

[8] A. Ouammi, R. Sacile and A. Mimet, Wind Energy Potential in Liguria Region, Renewable and Sustainable Energy Reviews, Vol. 14, N1, pp. 289 - 300, 2010.

[9] Y. Himri, S. Himri and A. Boudghene Stambouli, Assessing the Wind Energy Potential Projects in Algeria, Renewable and Sustainable Energy Reviews, Vol. 13, N8, pp. 2187 - 2191, 2009.

[10] E.J. Gumbel, Statistics of Extremes, Columbia University Press, 1958.

[11] C.G. Justus, W.R. Hargraves and A. Yalcin, Nationwide Assessment of Potential Output from Wind-Powered Generators, Journal of Applied Meteorology, Vol. 15, N7, pp. 673 - 678, 1976.

[12] C.G. Justus, W.R. Hargraves, A. Mikhail and D. Graber, Methods for Estimating Wind Speed Frequency Distribution, Journal of Applied Meteorology, Vol. 17, N3, pp. 350 - 353, 1978.

[13] L. Van Der Auwera, F. De Meyer and L.M. Malet, The Use of the Weibull Three Parameter Model for Estimating Mean Wind Power Density, Journal of Applied Meteorology, Vol. 19, N7, pp. 819 - 825, 1980.