摘要
通过对直驱式风电机组运行状态的分析可知:在风电场中,桨距角大于0的机组运行于额定工作点,而其余风电机组的运行点可由电磁转矩、发电机转速这两个状态变量反映。由此,提出了一种以风电机组具有相同或相近运行点为分群原则的风电场等值建模方法:首先将桨距角大于0的机组归为一组,再以电磁转矩、发电机转速作为分群指标利用K-means聚类算法进行分群计算,最后采用容量加权法计算分群后等值机组的参数并考虑了风电场内集电系统的影响。为了进行对比分析,在Matlab/Simulink平台上搭建了风电场的详细模型、以状态变量分群的等效模型及以风速分群的等效模型,仿真结果验证了所提出的建模方法能更准确地反映风电场在风速波动及电网故障情况下的动态特性。
The analysis on the operation state of direct-driven wind turbines revealed that in the wind farm,the units with pitch angle greater than 0 run at the rated operating point,while the operating points of other wind turbines are reflected by the two state variables,electromagnetic torque and generator speed.Therefore,this paper proposed a wind farm equivalent modeling method based on the grouping principle that the wind turbines grouped together should have the same or similar operating points.Firstly,the units with the pitch angle greater than 0 were grouped into one group.Then,this paper took the electromagnetic torque and generator speed as the clustering indexes and conducted the calculation by K-means clustering algorithm.Finally,this paper adopted the capacity weighting method to calculate the parameters of the grouped equivalent unit and took into consideration the influence of the current collecting system in the wind farm.Three models were established on Matlab/Simulink platform in comparative analysis,including the detailed model of wind farm,the equivalent model of state variable grouping and the equivalent model of wind speed grouping.The simulation results verify that the proposed modeling method is more accurate in reflecting the dynamic characteristics of wind farms under wind speed fluctuations and power grid faults.
作者
颜湘武
李君岩
YAN Xiangwu;LI Junyan(Key Laboratory of Distributed Energy Storage and Microgrid of Hebei Province,North China Electric Power University,Baoding 071003,China)
出处
《华北电力大学学报(自然科学版)》
CAS
北大核心
2019年第5期1-7,共7页
Journal of North China Electric Power University:Natural Science Edition
基金
河北省自然科学基金项目(E20185021134)
关键词
直驱永磁同步风电机组
风电场
状态变量
K-MEANS算法
direct-drive permanent magnet synchronous wind turbines
wind farm
state variable
K-means algorithm