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基于蜉蝣算法的变间距规则排布海上风电场微观选址优化方法

Optimization method for micro-siting of offshore wind farms based on the mayfly algorithm with variable spacing rules
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摘要 我国海上风电正处于快速发展阶段,但如何进一步提升机组能效和降低开发成本仍然是当前的关键规划问题。文章提出了一种基于七参数的非等间距规则排布优化方法,通过引入群体优化算法——蜉蝣算法,对该布局方法的关键参数进行优化,从而实现对海上风电场规则布局的整体优化。通过案例验证了所提优化方法的有效性。文章研究的微观选址规则布局优化方法为大型风电基地的规划设计提供了有价值的参考,能够在风电基地场址划分和布局设计等工作中发挥指导作用。 China’s offshore wind power is currently in a rapid development stage,but improving turbine efficiency and reducing development costs remain key planning challenges.This paper proposes a non-equidistant rule-based layout optimization method based on seven parameters.By introducing a group optimization algorithm—the Mayfly Algorithm—key parameters of the layout method are optimized to achieve overall optimization of the offshore wind farm’s regular layout.The effectiveness of the proposed optimization method is verified through case studies.The micro-siting rule-based layout optimization method studied in this paper provides valuable insights for the planning and design of large-scale wind farm bases and can serve as a guide in tasks such as site division and layout design for wind power projects.
作者 孙宇 许昌 韩星星 蒋小雪 谭康辉 SUN Yu;XU Chang;HAN Xingxing;JIANG Xiaoxue;TAN Kanghui(Shandong Electric Power Engineering Consulting Institute Co.,Ltd.,Jinan 250013,China;College of New Energy,Hohai University,Changzhou 213200,China)
出处 《新能源科技》 2025年第1期28-34,共7页 New Energy Science and Technology
基金 国家自然基金(52106238) 中国博士后科学基金资助项目(2024M760739) 中央高校基本科研业务费专项资金资助(B240201171)。
关键词 海上风电场 微观选址优化 蜉蝣算法 规则排布 offshore wind farm micro-siting optimization mayfly algorithm regular layout arrangement
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  • 1张义斌,王伟胜.风电场输出功率的概率分布及其应用[J].电力设备,2004,5(8):38-40. 被引量:18
  • 2丁明,吴义纯,张立军.风电场风速概率分布参数计算方法的研究[J].中国电机工程学报,2005,25(10):107-110. 被引量:220
  • 3王成山,郑海峰,谢莹华,陈恺.计及分布式发电的配电系统随机潮流计算[J].电力系统自动化,2005,29(24):39-44. 被引量:289
  • 4胡泽春,王锡凡,张显,王秀丽.考虑线路故障的随机潮流[J].中国电机工程学报,2005,25(24):26-33. 被引量:79
  • 5迟永宁,刘燕华,王伟胜,陈默子,戴慧珠.风电接入对电力系统的影响[J].电网技术,2007,31(3):77-81. 被引量:502
  • 6COELLO COELLO C A. Theoretical and numerical constraint- handling techniques used with evolutionary algorithms: a survey of the state of the art[J]. Computer Methods in Applied Mechanics and Engineering, 2002, 191(11/12): 1245- 1287.
  • 7MOSETTI G, POLONI C, DIVIACCO B. Optimization of wind turbine positioning in large wind farms by means of a genetic algorithm[J]. Wind Engineering Industrial Aerodynamic, 1994, 51 ( 1): 105 - 116.
  • 8GRADY S A, HUSSAINI M Y, ABDULLAH M M. Placement of wind turbines using genetic algorithms[J]. Renewable Energy, 2005, 30(2): 259 - 270.
  • 9WAN C Q, WANG J, YANG G, et al. Optimal micro-siting of wind turbines by genetic algorithms based on improved wind and turbine models[C] //The 48th IEEE Conference on Decision and Control Held Jointly with 2009 28th Chinese Control Conference. Piscataway: Institute of Electrical and Electronics Engineers Inc, 2009:5092 - 5096.
  • 10WAN C Q, WANG J, YANG G, et al. Optimal siting of wind turbines using real-coded genetic algorithms[C]//Proceedings of European Wind Energy Association Conference and Exhibition. Marseille, France, 2009. http:llwww.ewec2OO9proceedings.infol proceedings/index.php.

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