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Development of a Time-Domain Simulation Tool for Offshore Wind Farms
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  • Journal title : Journal of Power Electronics
  • Volume 15, Issue 4,  2015, pp.1047-1053
  • Publisher : The Korean Institute of Power Electronics
  • DOI : 10.6113/JPE.2015.15.4.1047
 Title & Authors
Development of a Time-Domain Simulation Tool for Offshore Wind Farms
Kim, Hyungyu; Kim, Kwansoo; Paek, Insu; Yoo, Neungsoo;
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A time-domain simulation tool to predict the dynamic power output of wind turbines in an offshore wind farm was developed in this study. A wind turbine model consisting of first or second order transfer functions of various wind turbine elements was combined with the Ainslie's eddy viscosity wake model to construct the simulation tool. The wind turbine model also includes an aerodynamic model that is a look up table of power and thrust coefficients with respect to the tip speed ratio and pitch angle of the wind turbine obtained by a commercial multi-body dynamics simulation tool. The wake model includes algorithms of superposition of multiple wakes and propagation based on Taylor's frozen turbulence assumption. Torque and pitch control algorithms were implemented in the simulation tool to perform max-Cp and power regulation control of the wind turbines. The simulation tool calculates wind speeds in the two-dimensional domain of the wind farm at the hub height of the wind turbines and yields power outputs from individual wind turbines. The NREL 5MW reference wind turbine was targeted as a wind turbine to obtain parameters for the simulation. To validate the simulation tool, a Danish offshore wind farm with 80 wind turbines was modelled and used to predict the power from the wind farm. A comparison of the prediction with the measured values available in literature showed that the results from the simulation program were fairly close to the measured results in literature except when the wind turbines are congruent with the wind direction.
Eddy Viscosity;Numerical Wake Model;Wake;Wind Farm;
 Cited by
Power regulation of upstream wind turbines for power increase in a wind farm, International Journal of Precision Engineering and Manufacturing, 2016, 17, 5, 665  crossref(new windwow)
R. A. Rivas, J. Clausen, K. S. Hansen, and L. E. Jensen, “Solving the turbine positioning problem for large offshore wind farms by simulated annealing,” Wind Energy, Vol. 33, No. 3, pp. 287-297, 2009.

S. Şişbot, Ö. Turgut, M. Tunç and Ü. Çamdalı, “Optimal positioning of wind turbines on gökçeada using multi‐objective genetic algorithm,” Wind Energy, Vol. 13, No. 4, pp. 297-306, 2010. crossref(new window)

A. Kusiak and Z. Song, “Design of wind farm layout for maximum wind energy capture,” Renewable Energy, Vol. 35, No. 3, pp. 685-694, Mar. 2010. crossref(new window)

L. Rademakers, H. Braam, T. Obdam and R. vd Pieterman, "Operation and maintenance cost estimator (OMCE) to estimate the future O&M costs of offshore wind farms," in Proc. European Offshore Wind 2009 Conference, Stockholm, Sweden, pp. 14-16, 2009.

G. Corten and P. Schaak, "Heat and flux," Patent Number WO2004111446 , 2003.

B. Arno, B. Edwin, K. Stoyan, S. Feike, and Ö. Hüseyin, “Wind farm design and active wake control,” Europe's Premier Wind Energy Event, 2014.

E. Bot, G. Corten, and P. Schaak, “A program to determine energy yield of wind turbines in a wind farm,” 2011.

P. Eecen and E. Bot, “Improvements to the ECN wind farm optimisation software ‘FarmFlow’,” the European Wind Energy Conference & Exhibition, 2010.

J. Pierik, U. Axelsson, E. Eriksson and D. Salomonsson, EeFarm II. Description, Testing and Application, Energy Research Centre of the Netherlands, 2009.

P. E. Réthoré, P. Fuglsang, T. J. Larsen, T. Buhl, and G. C. Larsen, “TOPFARM wind farm optimization tool,” Risø National Laboratory for Sustainable Energy, Technical University of Denmark, 2011

P. Fleming, P. Gebraad, J. van Wingerden, S. Lee, M. Churchfield, A. Scholbrock, J. Michalakes, K. Johnson and P. Moriarty, "The SOWFA super-controller: A high-fidelity tool for evaluating wind plant control approaches," in Proc. EWEA Annual Meeting, Vienna, Austria, 2013..

J. D. Grunnet, M. Soltani, T. Knudsen, M. N. Kragelund, and T. Bak, "Aeolus toolbox for dynamics wind farm model, simulation and control," in The European Wind Energy Conference & Exhibition, EWEC, 2010.

T. von Karman, "Progress in the statistical theory of turbulence," in Proc. Natl.Acad.Sci.U.S.A., Vol. 34, No. 11, pp. 530-539, Nov. 1948.

International Electrotechnical Committee, “IEC 61400-1: Wind Turbines part 1: Design requirements,” International Electrotechnical Commission, 2005.

J. F. Ainslie, “Calculating the flowfield in the wake of wind turbines,” J. Wind Eng. Ind. Aerodyn., Vol. 27, No. 1-3, pp. 213-224, Jan. 1988. crossref(new window)

I. Katic, J. Højstrup, and N. Jensen, "A simple model for cluster efficiency," in European Wind Energy Association Conference and Exhibition, pp. 407-410, 1986.

J. M. Jonkman, S. Butterfield, W. Musial, and G. Scott, Definition of a 5-MW reference wind turbine for offshore system development, National Renewable Energy Laboratory Golden, CO. 2009.

K. Kim, C. Lim, Y. Oh, I. Kwon, N. Yoo, and I. Paek, “Time-domain dynamic simulation of a wind turbine including yaw motion for power prediction,” International Journal of Precision Engineering and Manufacturing, Vol. 15, No. 10, pp. 2199-2203, Oct. 2014. crossref(new window)

A. Fluent, “Ansys Fluent Theory Guide,” ANSYS Inc., USA. 2011.