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Decomposition-Based Multi-Classifier-Assisted Evolutionary Algorithm for Bi-Objective Optimal Wind Farm Energy Capture

Author

Listed:
  • Hongbin Zhu

    (College of Engineering, Shantou University, Shantou 515063, China)

  • Xiang Gao

    (Industrial Training Centre, Shenzhen Polytechnic, Shenzhen 518055, China)

  • Lei Zhao

    (College of Engineering, Shantou University, Shantou 515063, China)

  • Xiaoshun Zhang

    (Foshan Graduate School of Innovation, Northeastern University, Foshan 528311, China)

Abstract

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison.

Suggested Citation

  • Hongbin Zhu & Xiang Gao & Lei Zhao & Xiaoshun Zhang, 2023. "Decomposition-Based Multi-Classifier-Assisted Evolutionary Algorithm for Bi-Objective Optimal Wind Farm Energy Capture," Energies, MDPI, vol. 16(9), pages 1-22, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:9:p:3718-:d:1133663
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    References listed on IDEAS

    as
    1. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Dai Cui & Fei Xu & Weichun Ge & Pengxiang Huang & Yunhai Zhou, 2020. "A Coordinated Dispatching Model Considering Generation and Operation Reserve in Wind Power-Photovoltaic-Pumped Storage System," Energies, MDPI, vol. 13(18), pages 1-24, September.
    3. Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2022. "A multi-objective maintenance strategy optimization framework for offshore wind farms considering uncertainty," Applied Energy, Elsevier, vol. 321(C).
    4. Niels Pynaert & Thomas Haas & Jolan Wauters & Guillaume Crevecoeur & Joris Degroote, 2023. "Wing Deformation of an Airborne Wind Energy System in Crosswind Flight Using High-Fidelity Fluid–Structure Interaction," Energies, MDPI, vol. 16(2), pages 1-16, January.
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