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Trajectory Optimization for Airborne Wind Energy Systems Based on a Multi-Strategy Improved Salp Swarm Algorithm

Author

Listed:
  • Yanjun Lv

    (Power China Guiyang Engineering Corporation Limited, Guiyang 550081, China)

  • Yan Pang

    (State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
    Liaoning Provincial Key Laboratory of Advanced Technologies for Aerospace Vehicles, Dalian 116024, China)

  • Zifeng Sun

    (Power China Guiyang Engineering Corporation Limited, Guiyang 550081, China)

  • Chenghao Zou

    (State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
    Liaoning Provincial Key Laboratory of Advanced Technologies for Aerospace Vehicles, Dalian 116024, China)

  • Yupeng Yang

    (State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
    Liaoning Provincial Key Laboratory of Advanced Technologies for Aerospace Vehicles, Dalian 116024, China)

Abstract

Airborne Wind Energy (AWE) systems offer benefits such as high altitude access to stronger and more stable winds, reduced environmental impact, and cost effective infrastructure. However, these systems face several challenges including complex flight trajectory optimization, limited control robustness, and unstable power generation. This paper focuses on optimizing the flight trajectory of a tethered rigid wing AWE system to maximize power generation. A mathematical model of the system is constructed, and a constrained trajectory optimization problem is formulated. The multiple shooting method is employed for discretization, and a Multi-Strategy Improved Salp Swarm Algorithm (MISSA) is proposed to solve the optimization problem. Simulation results indicate that MISSA can generate a closed optimal trajectory, significantly enhance power output, and demonstrate superior performance in addressing complex trajectory optimization challenges.

Suggested Citation

  • Yanjun Lv & Yan Pang & Zifeng Sun & Chenghao Zou & Yupeng Yang, 2025. "Trajectory Optimization for Airborne Wind Energy Systems Based on a Multi-Strategy Improved Salp Swarm Algorithm," Energies, MDPI, vol. 18(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5459-:d:1773067
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    References listed on IDEAS

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    1. Hao He & Xiaojing Niu & Xiaoyu Li & Yanfeng Cai & Leming Li & Xinwei Ye & Junhao Wang, 2025. "Characteristics of Wind Profiles for Airborne Wind Energy Systems," Energies, MDPI, vol. 18(9), pages 1-16, May.
    2. Bechtle, Philip & Schelbergen, Mark & Schmehl, Roland & Zillmann, Udo & Watson, Simon, 2019. "Airborne wind energy resource analysis," Renewable Energy, Elsevier, vol. 141(C), pages 1103-1116.
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