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All issues Volume 601 (2025) E3S Web Conf., 601 (2025) 00098 References
Open Access
Issue
E3S Web Conf.
Volume 601, 2025
The 3rd International Conference on Energy and Green Computing (ICEGC’2024)
Article Number 00098
Number of page(s) 12
DOI https://doi.org/10.1051/e3sconf/202560100098
Published online 16 January 2025
  1. Neubert, A. Shah, W. Schlez. “Maximum Yield From Symmetrical Wind Farm Layouts,” pp.1–4, DEWEK 2010 [Google Scholar]
  2. W. Wuc. “On the design of potential turbine positions for physics-informed optimization of wind farm layout,” Renewable Energy, Volume 164, pp. 1108–1120, February 2021 [CrossRef] [Google Scholar]
  3. T. Zilong. “Layout optimization of offshore wind farm considering spatially inhomogeneous wave loads,” Applied Energy, Volume 306, Part A, 15 January 2022 [Google Scholar]
  4. R. ReddyS. “Wind farm layout optimization (WindFLO): An advanced framework for fast wind farm analysis and optimization,” Applied Energy Volume 269, 1 July 2020 [Google Scholar]
  5. E. Antonini.” Continuous adjoint formulation for wind farm layout optimization: a 2D implementation,” Applied Energy Volume 228, 15, pp. 2333–2345, October 2018 [CrossRef] [Google Scholar]
  6. Y. KuoJ. “Wind farm layout optimization on complex terrains integrating a CFD wake model with mixed-integer programming,” Applied Energy, Volume 178, pp. 404–414, 15 September 2016 [CrossRef] [Google Scholar]
  7. K. Yang. “Wind farm layout optimization for wake effect uniformity,” Energy, Volume 183, pp. 983–995, 15 September 2019. [CrossRef] [Google Scholar]
  8. H. Long. “A data-driven evolutionary algorithm for wind farm layout optimization,” Energy, Volume 208, 1 October 2020 [Google Scholar]
  9. A.M. Boulamatsis. “Active control of wind turbines through varying blade tip sweep,” Renewable Energy, Volume 131, PP. 25–36, February 2019 [CrossRef] [Google Scholar]
  10. A. Cooperman. “Load monitoring for active control of wind turbines,” Renewable and Sustainable Energy Reviews Volume 41, PP. 189–201, January 2015 [CrossRef] [Google Scholar]
  11. H. Zong. “Experimental investigation and analytical modelling of active yaw control for wind farm power optimization,” Renewable Energy, Volume 170, PP. 1228–1244, June 2021 [CrossRef] [Google Scholar]
  12. H. Gu, J. Wang. “Irregular-shape wind farm micro-siting optimization,” Energy, pp. 57-535-44, 2013 [PubMed] [Google Scholar]
  13. P. Enevoldsen, M.Z. Jacobson “Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide,” Energy for Sustainable Development, PP. 60-40-51, 2021 [PubMed] [Google Scholar]
  14. J. Serrano González, M. Burgos Payán, J.M.R. Santos, F. González-Longatt “A review and recent developments in the optimal wind-turbine micrositing problem,” Renew Sustain Energy Rev, PP. 30-133-44. 2014 [Google Scholar]
  15. Renews. Biz. “Uniform turbine layout touted for NE US offshore wind,” Accessed 2 January 2020 [Google Scholar]
  16. N. Kirchner-Bossi, F. Porté-Agel “Realistic wind farm layout optimization through genetic algorithms using a Gaussian wake model,”. Energies [Google Scholar]
  17. J. Feng, W.Z. Shen “Co-optimization of the shape, orientation and layout of offshore wind farms,”. J Phys Conf Ser 1618(4) [Google Scholar]
  18. M. Clifton-Smith and D. Wood, “Optimisation of self-supporting towers for small wind turbines,” Wind Engineering, vol. 34, no. 5, pp. 561 578, 2010 [CrossRef] [Google Scholar]
  19. A. Kaveh, Applications of metaheuristic optimization algorithms in civil engineering. Springer, 2017 [CrossRef] [Google Scholar]
  20. G. Campione, “Simple equations for strength and deformability verification of tubular steel wind turbine towers,” Engineering Structures, vol. 228, pp. 111566, 2021 [CrossRef] [Google Scholar]
  21. J. Zhu, Z. Zhou, and X. Cai, “Multi-objective aerodynamic and structural integrated optimization design of wind turbines at the system level through a coupled blade-tower model,” Renewable Energy, vol. 150, pp. 523–537, 2020 [CrossRef] [Google Scholar]
  22. F. Ferroudji, “Numerical modal analysis of a 850 kw wind turbine steel tower,” International Review of Applied Sciences and Engineering, vol. 12, no. 1, pp. 10–18, 2021 [CrossRef] [Google Scholar]
  23. A. Furlanetto, H. M. Gomes, and F. S. de Almeida, “Design optimization of tapered steel wind turbine towers by qpso algorithm,” International Journal of Steel Structures, vol. 20, no. 5, pp. 1552–1563, 2020 [CrossRef] [Google Scholar]
  24. S. Al-Sanad, J. Parol, L. Wang, and A. Kolios, “Structuraloptimisation framework for onshore wind turbine towers considering multiple design constraints,” International Journal of Sustainable Energy, pp. 1–23, 2021 [Google Scholar]
  25. S. Al-Sanad, L. Wang, J. Parol, and A. Kolios, “Reliability- based design optimisation framework for wind turbine towers,” Renewable Energy, vol. 167, pp. 942–953, 2021 [CrossRef] [Google Scholar]
  26. Qiu, Yingning, Xu, Yili, Li, Jiawei, Feng, Yanhui, Yang, Wenxian, “Wind turbulence impacts to onshore and offshore wind turbines gearbox fatigue life,” Proceedings of the 3rd Renewable Power Generation Conference, pp. 1–5, September 2014 [Google Scholar]
  27. C. Hüseyin. “Numerical simulations of wind turbine wake interactions using actuator line and LES models,” thesis, September 2019 [Google Scholar]
  28. Li Li. Zhi Huang. “A novel three-dimensional analytical model of the added streamwise turbulence intensity for wind-turbine wakes,” Energy, 2021 [Google Scholar]
  29. N. Jensen “A note on wind generator interaction,” Tech.rep. Riso-M-2411, Ris National Laboratory, 1983 [Google Scholar]
  30. S. Emeis “A simple analytical wind park model considering atmospheric stability,” Wind Energy, pp. 459–469, 2010 [CrossRef] [Google Scholar]
  31. E.G. Antonini, K. Caldeira “Atmospheric pressure gradients and coriolis forces provide geophysical limits to power density of large wind farms,” Applied Energy, 2021 [Google Scholar]
  32. R.J. Barthelmie, K. Hansen, S.T. Frandsen, O. Rathmann, J.G. Schepers, W. Schlez, J. Phillips, K. Rados, A. Zervos, E.S. Politis, P.K. Chaviaropoulos “Modelling and measuring flow and wind turbine wakes in large wind farms offshore. Wind,” Energy, pp.431–444, 2009 [Google Scholar]
  33. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182–197, 2002 [CrossRef] [Google Scholar]
  34. N. Srinivas and K. Deb, “Muiltiobjective optimization using nondomi-nated sorting in genetic algorithms,” Evolutionary computation, vol. 2, no. 3, pp. 221–248, 1994 [CrossRef] [Google Scholar]
  35. A. Hertz, M. Widmer. “Guidelines for the use of meta- heuristics in combinatorial optimization,” Eur. J. Oper, pp. 151, 247-252, 2003 [Google Scholar]
  36. S. Vo. “The state of the art. In Proceedings of the Local Search for Planning and Scheduling,”, Springer, pp. 1–23, Germany, 21 August 2001 [Google Scholar]
  37. I.H. Osman, J.P. Kelly. “Meta-heuristics: An overview. In Meta-Heuristics: Theory and Applications,” Springer, pp. 1–21, New York, 1996 [Google Scholar]
  38. J. Kennedy, R.C. Eberhart. “Particle swarm optimization,” In Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, Volume 4, pp. 1942–1948, 27 November-1 December 1995 [Google Scholar]
  39. J. Kennedy. “Small worlds and mega-minds: Effects of neighborhood topology on particle swarm performance,” In Proceedings of the Congress on Evolutionary Computation, Washington, DC, USA, Volume 3, pp. 1931–1938, 6-9 July 1999 [Google Scholar]
  40. J. Kennedy, R. Mendes. “Population structure and particle swarm performance,” In Proceedings of the Congress on Evolutionary Computation, Volume 2, pp. 1671–1676., 12-17 May 2002 [Google Scholar]
  41. Joongjin Shin. “Wind Farm Layout Optimization Using a Metamodel and EA/PSO Algorithm in Korea Offshore,” Energies, 22 December 2020 [Google Scholar]

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