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A modified grey wolf optimizer for wind farm layout optimization problem

Author

Listed:
  • Shitu Singh

    (South Asian University)

  • Jagdish Chand Bansal

    (South Asian University)

Abstract

The optimal solution to the wind farm layout optimization problem helps in maximizing the total energy output from the given wind farm. Meta-heuristic algorithms are one of the famous methods for achieving this objective. In this paper, we focus on developing an efficient meta-heuristic based on the grey wolf optimizer for solving the wind farm layout optimization problem. The proposed algorithm is called enhanced chaotic grey wolf optimizer and it is introduced after validating it on a well-known benchmark set of 23 numerical optimization problems. By confirming its efficiency through these benchmarks, it is utilized for wind farm layout optimization. The proposed algorithm is comprised of four search strategies including a modified GWO search mechanism, modified control parameter, chaotic search, and adaptive re-initialization of poor solutions during the search. Two case studies of the wind farm layout optimization problem are considered for numerical experiments. Results are analyzed and compared with other state-of-the-art algorithms. The comparison indicates the efficiency of the proposed algorithm for solving numerical and wind farm layout optimization problems.

Suggested Citation

  • Shitu Singh & Jagdish Chand Bansal, 2024. "A modified grey wolf optimizer for wind farm layout optimization problem," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(10), pages 4750-4778, October.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:10:d:10.1007_s13198-024-02462-0
    DOI: 10.1007/s13198-024-02462-0
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    References listed on IDEAS

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    1. Long, Huan & Li, Peikun & Gu, Wei, 2020. "A data-driven evolutionary algorithm for wind farm layout optimization," Energy, Elsevier, vol. 208(C).
    2. Ju, Xinglong & Liu, Feng, 2019. "Wind farm layout optimization using self-informed genetic algorithm with information guided exploitation," Applied Energy, Elsevier, vol. 248(C), pages 429-445.
    3. Mittal, Prateek & Kulkarni, Kedar & Mitra, Kishalay, 2016. "A novel hybrid optimization methodology to optimize the total number and placement of wind turbines," Renewable Energy, Elsevier, vol. 86(C), pages 133-147.
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