首页|集群化发展模式下风电场预测、规划及控制关键技术综述

集群化发展模式下风电场预测、规划及控制关键技术综述

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随着我国风电行业的快速发展,规模化发展风电对能源转型的推进至关重要.然而,由于风电场所处的复杂环境和高昂的建设运营成本等因素,集群化发展模式下的风电场面临着一系列技术难点和挑战.因此,文中针对集群化发展模式下风电场的建设和运营,重点总结了相关的预测、规划及控制关键技术.文中围绕风电场集群的关键技术与研究思路进行了归纳总结;针对风电场集群的特点,包括风资源量化表征与预测技术、多风电场协同优化规划技术以及多风电场控制与运行技术进行了详细探讨,对于每个技术领域,分析了当前的研究现状和取得的成果;最后,阐明了风电场集群建设和运营关键技术领域的发展趋势,并指出了待解决的技术难点,通过总结这些技术的研究成果为我国风电的规模化、集群化开发提供参考.
Review of the key technologies of wind farm cluster prediction,planning and control
With the rapid development of wind industry in China,it plays an important role for the promotion of energy transition to exploit wind energy in a large-scale format.However,due to the complicated environment and high construction and operation cost,it faces a series of technical difficulties and challenges to develop the wind farms in the format of a cluster.In view of this,the prediction,planning,and control technologies related to the construction and operation of wind farm cluster are summarized in this paper.Specifically,the key technologies and research route of the wind farm cluster are summarized in this paper.According to the characteristics of the wind farm cluster,the quantitative characterization and prediction of wind resources,the coordinate optimal planning of multiple wind farms,and the control and operation of multiple wind farms are discussed in detail.The current research status and achievements in each technology field are analyzed in this paper.Lastly,the development trend of the construction and operation of wind farm cluster is illustrated and the technical difficulties to be addressed are pointed out in this paper.The summary of these technical research achievements can provide reference for the large-scale and clustered development of wind power in China.

wind farm clusterwind turbinecombined predictionjoint planningcoordinated controlartificial intelligence(AI)

陶思钰、江福庆

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南京航空航天大学自动化学院,江苏南京 211106

风电场集群 风电机组 组合预测 协同规划 协调控制 人工智能(AI)

国家自然科学基金资助项目江苏省卓越博士后计划中国博士后科学基金可再生能源并网全国重点实验室开放基金项目

522071112022ZB2-002023T160311NYB5120-2301636

2024

10.12158/j.2096-3203.2024.01.010

电力工程技术
江苏省电力公司 江苏省电机工程学会

电力工程技术

CSTPCD北大核心
影响因子：0.969
ISSN：2096-3203
年,卷(期)：2024.43(1)
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