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Blade layers optimization of wind turbines using FAST and improved PSO Algorithm

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  • Liao, C.C.
  • Zhao, X.L.
  • Xu, J.Z.

Abstract

Based on the blade layers, a multi-criteria constrained optimum design model for wind turbine blades is developed and programmed. This model is pursued with respect to minimum blade mass to reduce the cost of wind turbine production. Because the spar caps are the main parts to endure the loads in modern wind turbines, the thickness and the location of the layers on spar caps are chosen as the optimization variables. During the process of design, a lot of criteria need be satisfied. The maximum blade tip deflection is one of the most important criteria in the blade design. However, to get it, at least 130 load cases need be computed in engineering design, so the process is very time consuming. To decrease the computation time, the load case which presents the maximum tip deflection in the initial design is selected and calculated by FAST software in this model. Besides, an improved particle swarm optimization (PSO) algorithm, which has better optimization capability and efficiency than the original PSO algorithm, is used to search the optimum solution. To build this model based on the blade layers, a key module used to link the FAST and the improved PSO algorithm is programmed and named PreLayers. At last, this optimization model is applied in an MW wind turbine design. The optimal results have also been tested by FOCUS5. The computed results show that this model is efficient and can be an optimal design tool in the engineering design.

Suggested Citation

  • Liao, C.C. & Zhao, X.L. & Xu, J.Z., 2012. "Blade layers optimization of wind turbines using FAST and improved PSO Algorithm," Renewable Energy, Elsevier, vol. 42(C), pages 227-233.
    • Handle: RePEc:eee:renene:v:42:y:2012:i:c:p:227-233
    DOI: 10.1016/j.renene.2011.08.011
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    References listed on IDEAS

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    1. Kong, C. & Bang, J. & Sugiyama, Y., 2005. "Structural investigation of composite wind turbine blade considering various load cases and fatigue life," Energy, Elsevier, vol. 30(11), pages 2101-2114.
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    2. Liang Lu & Minyan Zhu & Haijun Wu & Jianzhong Wu, 2022. "A Review and Case Analysis on Biaxial Synchronous Loading Technology and Fast Moment-Matching Methods for Fatigue Tests of Wind Turbine Blades," Energies, MDPI, vol. 15(13), pages 1-34, July.
    3. Jie Zhu & Xin Cai & Pan Pan & Rongrong Gu, 2014. "Multi-Objective Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using the Non-Dominated Sorting Genetic Algorithm II and Finite Element Method," Energies, MDPI, vol. 7(2), pages 1-15, February.
    4. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    5. Jie Zhu & Xin Cai & Rongrong Gu, 2016. "Aerodynamic and Structural Integrated Optimization Design of Horizontal-Axis Wind Turbine Blades," Energies, MDPI, vol. 9(2), pages 1-18, January.
    6. Lu, Liang & Wu, Haijun & Wu, Jianzhong, 2021. "A case study for the optimization of moment-matching in wind turbine blade fatigue tests with a resonant type exciting approach," Renewable Energy, Elsevier, vol. 174(C), pages 769-785.
    7. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    8. Shen, Xin & Chen, Jin-Ge & Zhu, Xiao-Cheng & Liu, Peng-Yin & Du, Zhao-Hui, 2015. "Multi-objective optimization of wind turbine blades using lifting surface method," Energy, Elsevier, vol. 90(P1), pages 1111-1121.
    9. Xin Cai & Jie Zhu & Pan Pan & Rongrong Gu, 2012. "Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method," Energies, MDPI, vol. 5(11), pages 1-14, November.
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    12. Long Wang & Ran Han & Tongguang Wang & Shitang Ke, 2018. "Uniform Decomposition and Positive-Gradient Differential Evolution for Multi-Objective Design of Wind Turbine Blade," Energies, MDPI, vol. 11(5), pages 1-19, May.

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