ARCHIVES
Original Article
Optimal Placement of Wind Turbines in a Wind Farm using Sparrow Search Algorithm
Usuripata Aswini1
Y. Krishnapriya2
1 2 Department of EEE, Anantha Lakshmi Institute of Technology and Sciences (Autonomous), Ananthapuramu, Andhra Pradesh, India.
Published Online: May-June 2026
Pages: 103-110
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20260603015References
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pp. 259–270, 2005, doi: 10.1016/j.renene.2004.05.007.
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Neurocomputing, vol. 70, no. 16–18, pp. 2651–2658, 2007, doi: 10.1016/j.neucom.2006.05.017.
6. P. Hou, W. Hu, M. Soltani, and Z. Chen, “Optimized Placement of Wind Turbines in Large-Scale Offshore Wind Farm Using Particle
Swarm Optimization Algorithm,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1272–1282, 2015, doi: 10.1109/TSTE.2015.2429912.
7. X. Wu, W. Hu, Q. Huang, C. Chen, Z. Chen, and F. Blaabjerg, “Optimized placement of onshore wind farms considering topography,”
Energies, vol. 12, no. 15, 2019, doi: 10.3390/en12152944.
8. J. Feng and W. Z. Shen, “Solving the wind farm layout optimization problem using random search algorithm,” Renew. Energy, vol. 78,
pp. 182–192, 2015, doi: 10.1016/j.renene.2015.01.005.
9. A. Emami and P. Noghreh, “New approach on optimization in placement of wind turbines within wind farm by genetic algorithms,” Renew.
Energy, vol. 35, no. 7, pp. 1559–1564, Jul. 2010, doi: 10.1016/j.renene.2009.11.026.
10. Y. Chen, H. Li, K. Jin, and Q. Song, “Wind farm layout optimization using genetic algorithm with different hub height wind turbines,”
Energy Convers. Manag., vol. 70, pp. 56–65, Jun. 2013, doi: 10.1016/j.enconman.2013.02.007.
11. R. Shakoor, M. Y. Hassan, A. Raheem, N. Rasheed, and M. N. im Mohd Nasir, “Wind farm layout optimization by using Definite Point
selection and genetic algorithm,” in Conference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014,
Mar. 2014, pp. 191–195, doi: 10.1109/PECON.2014.7062439.
12. X. Gao, H. Yang, L. Lin, and P. Koo, “Wind turbine layout optimization using multi-population genetic algorithm and a case study in
Hong Kong offshore,” J. Wind Eng. Ind. Aerodyn., vol. 139, pp. 89–99, Apr. 2015, doi: 10.1016/j.jweia.2015.01.018.
13. Y. Eroĝlu and S. U. Seçkiner, “Design of wind farm layout using ant colony algorithm,” Renew. Energy, vol. 44, pp. 53–62, Aug. 2012,
doi: 10.1016/j.renene.2011.12.013.
14. J. Castro Mora, J. M. Calero Barón, J. M. Riquelme Santos, and M. Burgos Payán, “An evolutive algorithm for wind farm optimal design,”
Neurocomputing, vol. 70, no. 16–18, pp. 2651–2658, Oct. 2007, doi: 10.1016/j.neucom.2006.05.017.
15. G. Marmidis, S. Lazarou, and E. Pyrgioti, “Optimal placement of wind turbines in a wind park using Monte Carlo simulation,” Renew.
Energy, vol. 33, no. 7, pp. 1455–1460, Jul. 2008, doi: 10.1016/j.renene.2007.09.004.
16. S. Chowdhury, J. Zhang, A. Messac, and L. Castillo, “Unrestricted wind farm layout optimization (UWFLO): Investigating key factors
influencing the maximum power generation,” Renew. Energy, vol. 38, no. 1, pp. 16–30, Feb. 2012, doi: 10.1016/j.renene.2011.06.033.
17. B. DuPont, J. Cagan, and P. Moriarty, “An advanced modeling system for optimization of wind farm layout and wind turbine sizing using
a multi-level extended pattern search algorithm,” Energy, vol. 106, pp. 802–814, Jul. 2016, doi: 10.1016/j.energy.2015.12.033.
18. P. P. Biswas, P. N. Suganthan, and G. A. J. Amaratunga, “Optimal placement of wind turbines in a windfarm using L-SHADE algorithm,”
2017 IEEE Congr. Evol. Comput. CEC 2017 - Proc., no. 1, pp. 83–88, 2017, doi: 10.1109/CEC.2017.7969299.
19. X. Ju and F. Liu, “Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation,” Appl.
Energy, vol. 248, no. April, pp. 429–445, 2019, doi: 10.1016/j.apenergy.2019.04.084.
20. H. Long, P. Li, and W. Gu, “A data-driven evolutionary algorithm for wind farm layout optimization,” Energy, vol. 208, p. 118310, 2020,
doi: 10.1016/j.energy.2020.118310.
21. J. Xue and B. Shen, “A novel swarm intelligence optimization approach: sparrow search algorithm,” Syst. Sci. Control Eng., vol. 8, no. 1,
pp. 22–34, Jan. 2020, doi: 10.1080/21642583.2019.1708830.
22. S. Pookpunt and W. Ongsakul, “Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-
varying acceleration coefficients,” Renew. Energy, vol. 55, pp. 266–276, 2013, doi: 10.1016/j.renene.2012.12.005.
23. N. O. Jensen, “A note on wind generator interaction,” Risø-M-2411 Risø Natl. Lab. Roskilde, pp. 1–16, 1983, [Online]. Available:
http://www.risoe.dk/rispubl/VEA/veapdf/ris-m-2411.pdf.
26, no. 1, pp. 56–63, 2019, doi: 10.17559/TV-20170725231351.
2. H. Rezk, A. Fathy, A. A. Zaki Diab, and M. Al-Dhaifallah, “The application of water cycle optimization algorithm for optimal placement
of wind turbines in wind farms,” Energies, vol. 12, no. 22, 2019, doi: 10.3390/en12224335.
3. G. Mosetti, C. Poloni, and D. Diviacco, “Optimization of wind turbine positioning in large wind farms by means of a Genetic algorithm. J
Wind Eng Ind Aerody 51:105–116,” J. Wind Eng. Ind. Aerodyn., vol. 51, no. 51, pp. 105–116, 1994.
4. S. A. Grady, M. Y. Hussaini, and M. M. Abdullah, “Placement of wind turbines using genetic algorithms,” Renew. Energy, vol. 30, no. 2,
pp. 259–270, 2005, doi: 10.1016/j.renene.2004.05.007.
5. J. Castro Mora, J. M. Calero Barón, J. M. Riquelme Santos, and M. Burgos Payán, “An evolutive algorithm for wind farm optimal design,”
Neurocomputing, vol. 70, no. 16–18, pp. 2651–2658, 2007, doi: 10.1016/j.neucom.2006.05.017.
6. P. Hou, W. Hu, M. Soltani, and Z. Chen, “Optimized Placement of Wind Turbines in Large-Scale Offshore Wind Farm Using Particle
Swarm Optimization Algorithm,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1272–1282, 2015, doi: 10.1109/TSTE.2015.2429912.
7. X. Wu, W. Hu, Q. Huang, C. Chen, Z. Chen, and F. Blaabjerg, “Optimized placement of onshore wind farms considering topography,”
Energies, vol. 12, no. 15, 2019, doi: 10.3390/en12152944.
8. J. Feng and W. Z. Shen, “Solving the wind farm layout optimization problem using random search algorithm,” Renew. Energy, vol. 78,
pp. 182–192, 2015, doi: 10.1016/j.renene.2015.01.005.
9. A. Emami and P. Noghreh, “New approach on optimization in placement of wind turbines within wind farm by genetic algorithms,” Renew.
Energy, vol. 35, no. 7, pp. 1559–1564, Jul. 2010, doi: 10.1016/j.renene.2009.11.026.
10. Y. Chen, H. Li, K. Jin, and Q. Song, “Wind farm layout optimization using genetic algorithm with different hub height wind turbines,”
Energy Convers. Manag., vol. 70, pp. 56–65, Jun. 2013, doi: 10.1016/j.enconman.2013.02.007.
11. R. Shakoor, M. Y. Hassan, A. Raheem, N. Rasheed, and M. N. im Mohd Nasir, “Wind farm layout optimization by using Definite Point
selection and genetic algorithm,” in Conference Proceeding - 2014 IEEE International Conference on Power and Energy, PECon 2014,
Mar. 2014, pp. 191–195, doi: 10.1109/PECON.2014.7062439.
12. X. Gao, H. Yang, L. Lin, and P. Koo, “Wind turbine layout optimization using multi-population genetic algorithm and a case study in
Hong Kong offshore,” J. Wind Eng. Ind. Aerodyn., vol. 139, pp. 89–99, Apr. 2015, doi: 10.1016/j.jweia.2015.01.018.
13. Y. Eroĝlu and S. U. Seçkiner, “Design of wind farm layout using ant colony algorithm,” Renew. Energy, vol. 44, pp. 53–62, Aug. 2012,
doi: 10.1016/j.renene.2011.12.013.
14. J. Castro Mora, J. M. Calero Barón, J. M. Riquelme Santos, and M. Burgos Payán, “An evolutive algorithm for wind farm optimal design,”
Neurocomputing, vol. 70, no. 16–18, pp. 2651–2658, Oct. 2007, doi: 10.1016/j.neucom.2006.05.017.
15. G. Marmidis, S. Lazarou, and E. Pyrgioti, “Optimal placement of wind turbines in a wind park using Monte Carlo simulation,” Renew.
Energy, vol. 33, no. 7, pp. 1455–1460, Jul. 2008, doi: 10.1016/j.renene.2007.09.004.
16. S. Chowdhury, J. Zhang, A. Messac, and L. Castillo, “Unrestricted wind farm layout optimization (UWFLO): Investigating key factors
influencing the maximum power generation,” Renew. Energy, vol. 38, no. 1, pp. 16–30, Feb. 2012, doi: 10.1016/j.renene.2011.06.033.
17. B. DuPont, J. Cagan, and P. Moriarty, “An advanced modeling system for optimization of wind farm layout and wind turbine sizing using
a multi-level extended pattern search algorithm,” Energy, vol. 106, pp. 802–814, Jul. 2016, doi: 10.1016/j.energy.2015.12.033.
18. P. P. Biswas, P. N. Suganthan, and G. A. J. Amaratunga, “Optimal placement of wind turbines in a windfarm using L-SHADE algorithm,”
2017 IEEE Congr. Evol. Comput. CEC 2017 - Proc., no. 1, pp. 83–88, 2017, doi: 10.1109/CEC.2017.7969299.
19. X. Ju and F. Liu, “Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation,” Appl.
Energy, vol. 248, no. April, pp. 429–445, 2019, doi: 10.1016/j.apenergy.2019.04.084.
20. H. Long, P. Li, and W. Gu, “A data-driven evolutionary algorithm for wind farm layout optimization,” Energy, vol. 208, p. 118310, 2020,
doi: 10.1016/j.energy.2020.118310.
21. J. Xue and B. Shen, “A novel swarm intelligence optimization approach: sparrow search algorithm,” Syst. Sci. Control Eng., vol. 8, no. 1,
pp. 22–34, Jan. 2020, doi: 10.1080/21642583.2019.1708830.
22. S. Pookpunt and W. Ongsakul, “Optimal placement of wind turbines within wind farm using binary particle swarm optimization with time-
varying acceleration coefficients,” Renew. Energy, vol. 55, pp. 266–276, 2013, doi: 10.1016/j.renene.2012.12.005.
23. N. O. Jensen, “A note on wind generator interaction,” Risø-M-2411 Risø Natl. Lab. Roskilde, pp. 1–16, 1983, [Online]. Available:
http://www.risoe.dk/rispubl/VEA/veapdf/ris-m-2411.pdf.
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