Abstract
The paper presents a comprehensive, complex, numerical, optimization methodology (computational framework) dedicated for supporting structures of small-scale wind turbines. The small wind turbine (SWT) supporting structure is one of the key components determining the cost of such a device. Therefore, the supporting structure optimization will allow cost reduction and, hence, popularization of these devices around the world. The presented methodology is based on the following: single-objective (aggregation-approach to multi-objective problem) evolutionary algorithm driven optimization, finite-element structural analyses, estimation of wind energy capture efficiency (coupled aero-servo-elastic numerical simulations), and economic evaluation (based on real meteorological data). Then, the methodology is proposed for a guy-wired mast structure of an arbitrary chosen SWT model. The optimization of chosen design features of the structure is performed and as a result the optimal solution for given assumptions is presented and scaling factor for that case is identified (total mass of the foundations). The successful use of combined numerical methods (genetic algorithms, FE method analyses, coupled aero-servo-elastic numerical simulations, pre-/post-processing scripts, and economic evaluation models) is the main novelty of this work.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Gsänger S, Pitteloud JD (2015) Small wind world report summary 2015. WWEA. http://small-wind.org/wp-content/uploads/2014/12/Summary_SWWR2015_online.pdf. Accessed 20 July 2015
Grijalva S, Umer Tariq M (2011) Prosumer-based smart grid architecture enables a flat, sustainable electricity industry 978-1-61284-220-2/11/$26.00 ©2011 IEEE
International Electrotechnical Commission (2013) IEC 61400-2: wind turbines—part 2: small wind turbines. ISBN 978-2-8322-1284-4
Bukala J, Damaziak K, Kroszczynski K, Krzeszowiec M, Malachowski J (2015) Investigation of parameters influencing the efficiency of small wind turbines. J Wind Eng Ind Aerodyn 146:29–38. https://doi.org/10.1016/j.jweia.2015.06.017
Gasch R, Twele J (2012) Wind power plants: fundamentals, design, construction and operation, 2nd edn. Springer, Berlin ISBN 978-3-642-22937-4
Srinivas N, Deb K (1994) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2(3):221–248. https://doi.org/10.1162/evco.1994.2.3.221
Chehouri A, Younes R, Ilinca A, Perron J (2016) Wind turbine design: multi-objective optimization. Wind turbines—design, control and applications, edited by Abdel Ghani Aissaoui and Ahmed Tahor. InTech, Rijeka, Croatia, Chapter 6:121–147. https://doi.org/10.5772/63481
Schwefel H-PP (1993) Evolution and optimum seeking: the six generation. Wiley, Hoboken ISBN 0471571482
Hajela P, Lin C-Y (1992) Genetic search strategies in multicriterion optimal design. Struct Optim 4(2):99–107. https://doi.org/10.1007/BF01759923
Alexandrov NM, Hussaini MY (1997) Multidisciplinary design optimization: state of the art. 80, SIAM, United States. ISBN 978-0898713596
Deb K (2001) Multi-objective optimization using evolutionary algorithms, vol 16. Wiley, Hoboken ISBN 047187339X
Muskulus M, Schafhirt S (2014) Design optimization of wind turbine support structures—a review. J Ocean Wind Energy 1:12–22
Chehouri A, Younes R, Ilinca A, Perron J (2015) Review of performance optimization techniques applied to wind turbines. Appl Energy 142:361–388. https://doi.org/10.1016/j.apenergy.2014.12.043
Negm HM, Maalawi KY (2000) Structural design optimization of wind turbine towers. Comput Struct 74:649–666. https://doi.org/10.1016/S0045-7949(99)00079-6
Uys PE, Farkas J, Jarmai K, van Tonder F (2007) Optimisation of a steel tower for a wind turbine structure. Eng Struct 29:1337–1342. https://doi.org/10.1016/j.engstruct.2006.08.011
Nicholson JC, Arora JS, Goyal D, Tinjum JM (2013) Multi-objective structural optimization of wind turbine tower and foundation systems using isight: a process automation and design exploration software. 10th World Congress on Structural and Multidisciplinary Optimization, 19–24 May 2013, Orlando, Florida, USA
Yoshida S (2006) Wind turbine tower optimization method using a genetic algorithm. Wind Eng 30:453–470. https://doi.org/10.1260/030952406779994150
Yıldırım S, Özkol I (2010) Wind turbine tower optimization under various requirements by using genetic algorithm. Engineering 2:641–647. https://doi.org/10.4236/eng.2010.28082
Blachowski B, Gutkowski W (2016) Effect of damaged circular flange-bolted connections on behaviour of tall towers, modelled by multilevel substructuring. Eng Struct 111:93–103. https://doi.org/10.1016/j.engstruct.2015.12.018
Bottasso CL, Campagnolo F, Croce A (2012) Multi-disciplinary constrained optimization of wind turbines. Multibody Syst Dyn 27(1):21–53. https://doi.org/10.1007/s11044-011-9271-x
Wang L, Wang T, Wu J, Chen G (2017) Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design. Energy 120:346–361. https://doi.org/10.1016/j.energy.2016.11.087
Barnes RH, Morozov EV (2016) Structural optimisation of composite wind turbine blade structures with variations of internal geometry configuration. Compos Struct 152:158–167. https://doi.org/10.1016/j.compstruct.2016.05.013
Fagan EM, Flanagan M, Leen SB, Flanagan T, Doyle A, Goggins J (2017) Physical experimental static testing and structural design optimisation for a composite wind turbine blade. Compos Struct 164:90–103. https://doi.org/10.1016/j.compstruct.2016.12.037
Kusiak A, Zhang ZJ, Li MY (2010) Optimization of wind turbine performance with data-driven models. IEEE Trans Sustain Energy 1(2):66–76. https://doi.org/10.1109/TSTE.2010.2046919
Chen J, Shen WZ, Wang Q, Pang X, Li S, Guo X (2013) Structural optimization study of composite wind turbine blade. Mater Des 46:247–255. https://doi.org/10.1016/j.matdes.2012.10.036
Huang J, Yuan Y, Wang Z, Qi Z, Xing C, Gao J (2018) A global-to-local registration and error evaluation method of blade profile lines based on parameter priority. Int J Adv Manuf Technol 94:3829–3839. https://doi.org/10.1007/s00170-017-1125-0
Pourrajabian A, Afshar PAN, Ahmadizadeh M, Wood D (2016) Aero-structural design and optimization of a small wind turbine blade. Renew Energy 87:837–848. https://doi.org/10.1016/j.renene.2015.09.002
Vucina D, Marinic-Kragic I, Milas Z (2016) Numerical models for robust shape optimization of wind turbine blades. Renew Energy 87:849–862. https://doi.org/10.1016/j.renene.2015.10.040
Tang X, Huang X, Peng R, Liu X (2015) A direct approach of design optimization for small horizontal axis wind turbine blades. Procedia CIRP 36:12–16. https://doi.org/10.1016/j.procir.2015.01.047
Vitale AJ, Rossi AP (2008) Computational method for the design of wind turbine blades. Int J Hydrog Energy 33:3466–3470. https://doi.org/10.1016/j.ijhydene.2008.04.054
Olasek K, Karczewski M, Lipian M, Wiklak P, Jozwik K (2016) Wind tunnel experimental investigations of a diffuser augmented wind turbine model. Int J Numer Methods Heat Fluid Flow 26:2033–2047. https://doi.org/10.1108/HFF-06-2015-0246
Asl HJ, Yoon J (2016) Power capture optimization of variable-speed wind turbines using an output feedback controller. Renew Energy 86:517–525. https://doi.org/10.1016/j.renene.2015.08.040
Gao R, Gao Z (2016) Pitch control for wind turbine systems using optimization, estimation and compensation. Renew Energy 91:501–515. https://doi.org/10.1016/j.renene.2016.01.057
Ayadi M, Derbe N (2017) Nonlinear adaptive backstepping control for variable-speed wind energy conversion system-based permanent magnet synchronous generator. Int J Adv Manuf Technol 92:39–46. https://doi.org/10.1007/s00170-017-0098-3
Akbar MA, Mustafa V (2016) A new approach for optimization of vertical axis wind turbines. J Wind Eng Ind Aerodyn 153:34–45. https://doi.org/10.1016/j.jweia.2016.03.006
Kear M, Evans B, Ellis R, Rolland S (2016) Computational aerodynamic optimisation of vertical axis wind turbine blades. Appl Math Model 40:1038–1051. https://doi.org/10.1016/j.apm.2015.07.001
Marinic-Kragic I, Vucina D, Milas Z (2018) Numerical workflow for 3D shape optimization and synthesis of vertical-axis wind turbines for specified operating regimes. Renew Energy 115:113–127. https://doi.org/10.1016/j.renene.2017.08.030
Clifton-Smith MJ, Wood DH (2010) Optimisation of self-supporting towers for small wind turbines. Wind Eng 34:561–578
Deb K (2014) Multi-objective optimization. Search methodologies. Introductory tutorials in optimization and decision support techniques. Springer, Boston, pp 403–449
Stander N, Roux W, Basudhar A, Eggleston T, Goel T, Craig K (2014) LS-OPT® user’s manual. A Design optimization and probabilistic analysis tool for the engineering analyst. Copyright© LIVERMORE SOFTWARE TECHNOLOGY CORPORATION
MSC.Software Corporation (2003) MSC.Nastran 2004—reference manual. Printed in USA, ©2003
Buhl ML, Manjock A (2006) A comparison of wind turbine aeroelastic codes used for certification. American Institute of Aeronautics and Astronautics. http://www.nrel.gov/docs/fy06osti/39113.pdf. Accessed 19 March 2015
Brusca S, Lanzafame R, Messina M (2014) Flow similitude laws applied to wind turbines through blade element momentum theory numerical codes. Int J Energy Environ Eng 5:313–322. https://doi.org/10.1007/s40095-014-0128-y
Moriarty PJ, Hansen AC (2015) AeroDyn theory manual. National Renewable Energy Laboratory. http://www.nrel.gov/docs/fy05osti/36881.pdf. Accessed 20 March 2015
Belytschko T, Liu WK, Moran B (2000) Nonlinear finite elements for continua and structures. Wiley, Chichester, pp 317–337 ISBN 978-1-118-63270-3
Bilir L, Imir M, Devrim Y, Albostan A (2015) Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function. Int J Hydrog Energy 40:15301–15310. https://doi.org/10.1016/j.ijhydene.2015.04.140
Drew DR, Barlow JF, Cockerill TT, Vahdati MM (2015) The importance of accurate wind resource assessment for evaluating the economic viability of small wind turbines. Renew Energy 77:493–500. https://doi.org/10.1016/j.renene.2014.12.032
Holland JH (1975) Adaptation in natural and artificial systems. Univ. Michigan, Ann Arbor
Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Boston
Bendsoe MP, Mota Soares CA (1992) Topology design of structures. Proceedings of the NATO Advanced Research Workshop on Topology Design of Structures, Sesimbra
Mitsuo G, Runwei C (2000) Genetic algorithms and engineering optimization. Wiley, Hoboken
Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim 26:369–395. https://doi.org/10.1007/s00158-003-0368-6
Lagaros ND, Papadrakakis M, Kokossalakis G (2002) Structural optimization using evolutionary algorithms. Comput Struct 80:571–589. https://doi.org/10.1016/S0045-7949(02)00027-5
Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1):122–128. https://doi.org/10.1109/TSMC.1986.289288
Somers DM (2005) The S833, S834, and S835 Airfoils. Subcontract report. National Renewable Energy Laboratory. https://wind.nrel.gov/airfoils/Documents/S833,S834,S835_Design.pdf. Accessed 20 May 2016
Bukala J, Damaziak K, Karimi HR, Malachowski J (2016) Aero-elastic coupled numerical analysis of small wind turbine—generator modeling. Wind Struct 23(6):577–594. https://doi.org/10.12989/was.2016.23.6.577
Grierson DE, Pak WH (1993) Optimal sizing, geometrical and topological design using a genetic algorithm. Struct Optim 6:151–159. https://doi.org/10.1007/BF01743506
Bukala J, Damaziak K, Karimi HR, Kroszczynski K, Krzeszowiec M, Malachowski J (2015) Modern small wind turbine design solutions comparison in terms of estimated cost to energy output ratio. Renew Energy 83:1166–1173. https://doi.org/10.1016/j.renene.2015.05.047
Funding
The study was supported by the Polish-Norwegian Research Programme operated by the National Centre for Research and Development under the Norwegian Financial Mechanism 2009–2014 in the frame of Project Contract No. Pol-Nor/200957/47/2013.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
About this article
Cite this article
Bukala, J., Damaziak, K., Karimi, H.R. et al. Evolutionary computing methodology for small wind turbine supporting structures. Int J Adv Manuf Technol 100, 2741–2752 (2019). https://doi.org/10.1007/s00170-018-2860-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-018-2860-6