Dynamic Equivalent Modeling and Simulation of Wind Farm Based on ASW-FCM Algorithm

doi:10.16182/j.issn1004731x.joss.20-0117

Abstract

Abstract: A new dynamic equivalent modeling method is proposed for the wind farm of directly-driven wind turbine with permanent magnet synchronous generator (D-PMSG). The wake effect and wind direction change between multiple wind turbines are considered to calculate the effective input wind speed, the effective wind speed, rotor speed, pitch angle and output power are selected as the multi-group indicators after analyzing the wind turbine operating characteristics. Considering the differences and relevance of the operating conditions between the turbines, an adaptive sample weighting fuzzy C-means clustering algorithm (ASW-FCM) is designed to optimally cluster the wind farm. The clustered wind turbine group equivalent model is established based on the principle that the output characteristics before and after the equivalence are equal. A wind farm system is selected as the example of modeling and simulation to verify the rationality and accuracy of the equivalent modeling method.

Key words: D-PMSG, wind farm, sample weighting, clustered grouping, equivalent modeling

CLC Number:

TM621

Li Mudan, Wang Yinsong. Dynamic Equivalent Modeling and Simulation of Wind Farm Based on ASW-FCM Algorithm[J]. Journal of System Simulation, 2020, 32(8): 1606-1616.

References 23

[1]	中华人民共和国国家发展和改革委员会. 风电发展“十三五”规划[R]. 北京: 中华人民共和国国家能源局, 2016.National Development and Reform Commission. “13th Five-Year Plan” for Wind Power Development[R]. Beijing: National Energy Administration of the People's Republic of China, 2016.
[2]	刘振亚. 全球能源互联网[M]. 北京: 中国电力出版社, 2015: 260-261.Liu Zhenya.Global Energy Internet[M]. Beijing: China Electric Power Press, 2015: 260-261.
[3]	宋恒东, 董学育. 风力发电技术现状及发展趋势[J]. 电工电气, 2015(1): 1-4.Song Hengdong, Dong Xueyu.Discussion on Present Situations and Development Trends of Wind Power Generation Technology[J]. Electrotechnics Electric, 2015(1):1-4.
[4]	孙元存, 刘三明, 王致杰, 等. 大规模风电接入对电力系统动态特性影响的研究[J]. 系统仿真技术, 2016, 12(2): 83-87.Sun Yuancun, Liu Sanming, Wang Zhijie, et al.Study of the Impact of Large-scale Wind Power Access to Dynamic Characteristics of Power System[J]. System Simulation Technology, 2016, 12(2): 83-87.
[5]	曹娜, 于群. 风电场建模技术及应用[M]. 北京: 机械工业出版社, 2015: 3.Cao Na, Yu Qun.Wind Farm Modeling Technology and Application[M]. Beijing: China Machine Press, 2015: 3.
[6]	胡杰, 余贻鑫. 电力系统动态等值参数聚合的实用方法[J]. 电网技术, 2006, 33(24): 26-30.Hu Jie, Yu Yixin.A Practical Method of Parameter Aggregation for Power System Dynamic Equivalence[J]. Power System Technology, 2006, 33(24): 26-30.
[7]	孙建锋, 焦连伟, 吴俊玲, 等. 风电场发电机动态等值问题的研究[J]. 电网技术, 2004, 28(7): 100-105.Sun Jianfeng, Jiao Lianwei, Wu Junling, et al.Research on Multi-Machine Dynamic Aggregation in Wind farm[J]. Power System Technology, 2004, 28(7): 100-105.
[8]	乔嘉赓, 鲁宗相, 闵勇. 风电场并网的新型实用等效方法[J]. 电工技术学报, 2009, 24(4): 209-214.Qiao Jiageng, Lu Zongxiang, Min Yong.New Dynamic Equivalence Method for Grid-Connected Wind Farm[J]. Transactions of China Electrotechnical Society, 2009, 24(4): 209-214.
[9]	Fernandez L M, Garcia C A, Saenz J R, et al.Equivalent Models of Wind Farms by Using Aggregated Wind Turbines and Equivalent Winds[J]. Energy Conversion and Management (S0196-8904), 2009, 50(3): 691-704.
[10]	曹娜, 于群. 风速波动情况下并网风电场内风电机组分组方法[J]. 电力系统自动化, 2012, 36(2): 42-46.Cao Na, Yu Qun.A Grouping Method for Wind Turbines in a Grid-connected Wind Farm During Wind Speed Fluctuation[J]. Automation of Electric Power System, 2012, 36(2): 42-46.
[11]	Liu H Z, Chen Z.Aggregated Modeling for Wind Farms for Power System Transient Stability Studies[C]. Proceedings of 2012 Asia-Pacific Power and Energy Engineering Conference (S2157-4839). Shanghai, China: IEEE, 2012: 1-6.
[12]	米增强, 苏勋文, 余洋, 等. 双馈机组风电场动态等效模型研究[J]. 电力系统自动化, 2010, 34(17): 72-77.Mi Zengqiang, Su Xunwen, Yu Yang, et al.Study on Dynamic Equivalence Model of Wind Farms with Wind Turbine Driven Doubly Fed Induction Generator[J]. Automation of Electric Power System, 2010, 34(17): 72-77.
[13]	黄伟, 张小珍. 基于特征分析的大规模风电场等值建模[J]. 电网技术, 2013, 37(8): 2271-2277.Huang Wei, Zhang Xiaozhen.Feature Analysis Based Equivalent Modeling for Large-Scale Wind Farms[J]. Power System Technology, 2013, 37(8): 2271-2277.
[14]	欧阳金鑫, 刁艳波, 郑迪, 等. 基于电流轨迹相似度的双馈风电机群电磁暂态同调分群方法[J]. 中国电机工程学报, 2017, 37(10): 2896-2904.Ouyang Jinxin, Diao Yanbo, Zheng Di, et al.A Clustering Method of Coherent Generators During Electromagnetic Transient Process Based on Similar Degrees of Current Trajectories for Doubly Fed Wind Farms[J]. Proceedings of the CSEE, 2017, 37(10): 2896-2904.
[15]	朱乾龙, 韩平平, 丁明, 等. 基于聚类-判别分析的风电场概率等值建模研究[J]. 中国电机工程学报, 2014, 34(28): 4770-4780.Zhu Qianlong, Han Pingping, Ding Ming, et al.Probabilistic Equivalent Model for Wind Farms Based on Clustering-discriminant Analysis[J]. Proceedings of the CSEE, 2014, 34(28): 4770-4780.
[16]	高峰, 赵东来, 周孝信. 直驱式风电机组风电场动态等值[J]. 电网技术, 2012, 36(12): 222-227.Gao Feng, Zhao Donglai, Zhou Xiaoxin.Dynamic Equivalent Algorithm for Wind Farm Composed of Direct-Drive Wind Turbines[J]. Power System Technology, 2012, 36(12): 222-227.
[17]	林俐, 潘险险. 基于分裂层次半监督谱聚类算法的风电场机群划分方法[J]. 电力自动化设备, 2015, 35(2): 8-14.Lin Li, Pan Xianxian.Wind turbine grouping based on semi-supervised split-hierarchical spectral clustering algorithm for wind farm[J]. Electric Power Automation Equipment, 2015, 35(2): 8-14.
[18]	Hansen A D, Jauch C, Sorensen P.Dynamic Wind Turbine Models in Power System Simulation Tool Digsilent[M]. Risø National Laboratory, Risø, Denmark, Tech. Rep. Risø-R-400(EN), Aug, 2007.
[19]	杨骏华. 直驱型永磁同步风电系统的控制策略研究[D].成都: 西华大学, 2017.Yang Junhua.Research on control strategy of direct drive permanent magnet synchronous wind power system[D]. Chengdu: Xihua University, 2017.
[20]	于永生, 冯延晖, 江红鑫, 等. 海上风电经VSC-MTDC并网研究[J]. 高压电器, 2015, 51(10): 24-33.Yu Yongsheng, Feng Yanhui, Jiang Hongxin, et al.Research on offshore wind farm integration via VSCMTDC[J]. High Voltage Apparatus, 2015, 51(10): 24-33.
[21]	赵兵, 汤涌, 张文朝. 感应电动机群单机等值算法研究[J]. 中国电机工程学报, 2009, 29(19): 43-49.Zhao Bing, Tang Yong, Zhang Wenchao.Study on single-unit equivalent algorithm of induction motor group[J]. Proceedings of the CSEE, 2009, 29(19): 43-49.
[22]	Xie X, Beni G.A Validity Measure for Fuzzy Clustering[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 1991, 13(8): 841-847.
[23]	Yu J, Cheng Q.The Search Range of Optimal Clustering Number in Fuzzy Cluster Method[J]. Sci China Ser E (S1006-9321), 2002, 32(2): 274-280.