IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i5p1262-d146387.html
   My bibliography  Save this article

Uniform Decomposition and Positive-Gradient Differential Evolution for Multi-Objective Design of Wind Turbine Blade

Author

Listed:
  • Long Wang

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Ran Han

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Tongguang Wang

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Shitang Ke

    (Jiangsu Key Laboratory of Hi-Tech Research for Wind Turbine Design, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

Convergence performance and optimization efficiency are two critical issues in the application of commonly used evolution algorithms in multi-objective design of wind turbines. A gradient-based multi-objective evolution algorithm is proposed for wind turbine blade design, based on uniform decomposition and positive-gradient differential evolution. In the uniform decomposition, uniformly distributed reference vectors are established in the objective space to maintain population diversity so that the population aggregations, which are commonly observed for wind turbine blade design using gradient-free algorithms, are minimized. The positive-gradient differential evolution is introduced for population evolution to increase optimization efficiency by guiding the evolutionary process and significantly reducing searching ranges of each individual. Two-, three- and four-objective optimizations of 1.5 MW wind turbine blades reveal that the proposed algorithm can deliver uniformly distributed optimal solutions in an efficient way, and has advantages over gradient-free algorithms in terms of convergence performance and optimization efficiency. These advantages increase with the optimization dimension, and the proposed algorithm is more suitable for optimizations of small size populations, thus remarkably enhancing the design efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1262-:d:146387
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/5/1262/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/5/1262/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sharafi, Masoud & ElMekkawy, Tarek Y. & Bibeau, Eric L., 2015. "Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio," Renewable Energy, Elsevier, vol. 83(C), pages 1026-1042.
    2. Abedi, S. & Alimardani, A. & Gharehpetian, G.B. & Riahy, G.H. & Hosseinian, S.H., 2012. "A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1577-1587.
    3. Christian Lücken & Benjamín Barán & Carlos Brizuela, 2014. "A survey on multi-objective evolutionary algorithms for many-objective problems," Computational Optimization and Applications, Springer, vol. 58(3), pages 707-756, July.
    4. 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.
    5. 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.
    6. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Yusta-Loyo, José M. & Domínguez-Navarro, José A. & Ramírez-Rosado, Ignacio J. & Lujano, Juan & Aso, Ismael, 2011. "Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage," Applied Energy, Elsevier, vol. 88(11), pages 4033-4041.
    7. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    8. Yamani Douzi Sorkhabi, Sami & Romero, David A. & Yan, Gary Kai & Gu, Michelle Dao & Moran, Joaquin & Morgenroth, Michael & Amon, Cristina H., 2016. "The impact of land use constraints in multi-objective energy-noise wind farm layout optimization," Renewable Energy, Elsevier, vol. 85(C), pages 359-370.
    9. Miller, Aaron & Chang, Byungik & Issa, Roy & Chen, Gerald, 2013. "Review of computer-aided numerical simulation in wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 25(C), pages 122-134.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gisela Pujol-Vazquez & Leonardo Acho & José Gibergans-Báguena, 2020. "Fault Detection Algorithm for Wind Turbines’ Pitch Actuator Systems," Energies, MDPI, vol. 13(11), pages 1-14, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Long & Wang, Tongguang & Wu, Jianghai & Chen, Guoping, 2017. "Multi-objective differential evolution optimization based on uniform decomposition for wind turbine blade design," Energy, Elsevier, vol. 120(C), pages 346-361.
    2. Wang, Zheng & Zeng, Tiansheng & Chu, Xuening & Xue, Deyi, 2023. "Multi-objective deep reinforcement learning for optimal design of wind turbine blade," Renewable Energy, Elsevier, vol. 203(C), pages 854-869.
    3. Song, Dongran & Fan, Xinyu & Yang, Jian & Liu, Anfeng & Chen, Sifan & Joo, Young Hoon, 2018. "Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method," Applied Energy, Elsevier, vol. 224(C), pages 267-279.
    4. Vianna Neto, Júlio Xavier & Guerra Junior, Elci José & Moreno, Sinvaldo Rodrigues & Hultmann Ayala, Helon Vicente & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2018. "Wind turbine blade geometry design based on multi-objective optimization using metaheuristics," Energy, Elsevier, vol. 162(C), pages 645-658.
    5. Tezer, Tuba & Yaman, Ramazan & Yaman, Gülşen, 2017. "Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 840-853.
    6. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
    7. Sharafi, Masoud & ElMekkawy, Tarek Y., 2015. "Stochastic optimization of hybrid renewable energy systems using sampling average method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1668-1679.
    8. Thé, Jesse & Yu, Hesheng, 2017. "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods," Energy, Elsevier, vol. 138(C), pages 257-289.
    9. Wang, Rui & Xiong, Jian & He, Min-fan & Gao, Liang & Wang, Ling, 2020. "Multi-objective optimal design of hybrid renewable energy system under multiple scenarios," Renewable Energy, Elsevier, vol. 151(C), pages 226-237.
    10. Yong Ma & Aiming Zhang & Lele Yang & Chao Hu & Yue Bai, 2019. "Investigation on Optimization Design of Offshore Wind Turbine Blades based on Particle Swarm Optimization," Energies, MDPI, vol. 12(10), pages 1-18, May.
    11. Zhang, Ye & Deng, Shuanghou & Wang, Xiaofang, 2019. "RANS and DDES simulations of a horizontal-axis wind turbine under stalled flow condition using OpenFOAM," Energy, Elsevier, vol. 167(C), pages 1155-1163.
    12. McKenna, R. & Ostman v.d. Leye, P. & Fichtner, W., 2016. "Key challenges and prospects for large wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1212-1221.
    13. Wang, Rui & Li, Guozheng & Ming, Mengjun & Wu, Guohua & Wang, Ling, 2017. "An efficient multi-objective model and algorithm for sizing a stand-alone hybrid renewable energy system," Energy, Elsevier, vol. 141(C), pages 2288-2299.
    14. Dhoot, Aditya & Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2021. "Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability," Energy, Elsevier, vol. 223(C).
    15. 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.
    16. Cao, Lichao & Ge, Mingwei & Gao, Xiaoxia & Du, Bowen & Li, Baoliang & Huang, Zhi & Liu, Yongqian, 2022. "Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines," Applied Energy, Elsevier, vol. 323(C).
    17. Meng, Hang & Lien, Fue-Sang & Yee, Eugene & Shen, Jingfang, 2020. "Modelling of anisotropic beam for rotating composite wind turbine blade by using finite-difference time-domain (FDTD) method," Renewable Energy, Elsevier, vol. 162(C), pages 2361-2379.
    18. Margielewicz, Jerzy & Gąska, Damian & Litak, Grzegorz & Wolszczak, Piotr & Yurchenko, Daniil, 2022. "Nonlinear dynamics of a new energy harvesting system with quasi-zero stiffness," Applied Energy, Elsevier, vol. 307(C).
    19. Jiaxin Lu & Weijun Wang & Yingchao Zhang & Song Cheng, 2017. "Multi-Objective Optimal Design of Stand-Alone Hybrid Energy System Using Entropy Weight Method Based on HOMER," Energies, MDPI, vol. 10(10), pages 1-17, October.
    20. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1262-:d:146387. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.