2025年3月30日 周日
首页 > 过刊浏览>2025年第40卷第1期 >1-18. DOI:10.19781/j.issn.1673-9140.2025.01.001
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大规模风电场协调控制架构及算法综述
作者:
作者单位:

(1.哈尔滨工业大学(威海) 新能源学院，山东 威海 264200;2.国网浙江省电力有限公司杭州市临安区供电公司，浙江 杭州 311300;3.华中科技大学电气与电子工程学院(强电磁工程与新技术国家重点实验室)，湖北 武汉 430074;4.华中科技大学电气与电子工程学院(电力安全与高效湖北省重点实验室)，湖北 武汉 430074)

通讯作者:

韩 佶（1993—），男，博士，副教授，主要从事新能源控制、电力系统运行、人工智能应用等方面的研究；E?mail:hanji@hit.edu.cn

中图分类号:

TM863

基金项目:

山东省自然科学基金青年基金（ZR2023QE315）


Review of coordinated control architectures and algorithms for large‑scale wind farms
Author:
Affiliation:

(1.College of New Energy, Harbin Institute of Technology, Weihai 264200, China; 2.Lin’an Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311300, China; 3.State Key Laboratory of Advanced Electromagnetic Engineering and Technology, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; 4.Hubei Key Laboratory of Electric Power Security and High Efficiency, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

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  • 参考文献 [135]
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    摘要:

    随着风电并网规模的增长，电力系统稳定性面临更多挑战，迫切需要高效的风电集群协调控制技术。首先分析频率控制、功率平滑控制、电压控制、最大功率输出控制等风电集群协调场景的基本原理；在此基础上，分析分散式、集中式和分布式风电集群协调控制框架的性能，探讨新型控制框架的技术特点和优势；接下来，综述风电集群协调控制实现算法，包括模糊算法、一致性算法、模型预测控制、交替方向乘子法、人工智能算法、群智能算法等，讨论不同算法的适用场景；最后，展望风电集群协调控制技术的发展趋势，指出跨学科融合的实现新思路。

    Abstract:

    With the growth of wind power grid-connected scale, power system stability is facing more challenges, necessitating efficient coordinated control techniques for wind power clusters. The basic principles of coordination in wind power cluster scenarios are analyzed, such as frequency control, power smoothing control, voltage control, and maximum power output control. On this basis, the coordinated control framework performance of decentralized, centralized, and distributed wind power clusters is analyzed, and the technical characteristics and advantages of new control frameworks are explored. Next, algorithms for coordinated control of wind power clusters are reviewed, including the fuzzy algorithm, consistency algorithm, model predictive control, alternating direction multiplier method, artificial intelligence algorithm, and swarm intelligence algorithm, and the applicable scenarios of different algorithms are discussed. Finally, the development trend of coordinated control techniques for wind power clusters is projected, and new insights into achieving interdisciplinary integration are provided.

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