摘要
针对已有双馈风电机组风电场动态等值所建模型存在准确度不够高,适应性不够强的缺点,提出了一种基于主成分分析(Principal component analysis,PCA)的改进人工蜂群算法(Improve artificial bee colony,IABC)的动态等值建模方法。首先,对双馈风力机进行数学建模,获取机组的的所有状态参数;其次,利用主成分分析法将变量间的冗余性与相关性进行处理,提取发电机定转子q轴电流、电磁转矩、发电机转速四个主导变量作为分群指标,使用IABC搜索最佳聚类中心;最后,运用K-means算法对风电场进行聚类分群处理,并在Matlab/Simulink平台进行仿真。仿真结果表明本文所建等值模型在风速扰动和系统侧故障工况下都有较好的准确性和适应性。
In view of the shortcomings of the existing doubly-fed induction generator(DFIG)wind farm dynamic equivalent model,such as the accuracy is not high enough and the adaptability is not strong enough,a dynamic equivalent modeling method based on principal component analysis(PCA)and improved artificial bee colony(IABC)is presented.Firstly,by modeling and analyzing the doubly-fed wind turbine,all the state variables that can represent the wind turbine are obtained.Secondly,principal component analysis is used to deal with the redundancy and correlation between variables,and four dominant variables,namely stator q-axis current,rotor q-axis current,electromagnetic torque and generator speed,are extracted as clustering indexes.IABC is used to search for the best clustering center.Finally,the K-means algorithm is used to cluster the wind farms,and the simulation is carried out on the Matlab/Simulink platform.The simulation results show that the equivalent model has good accuracy and adaptability under wind speed disturbance and system side fault conditions.
作者
张晓英
吴宏强
舒子江
王琨
陈伟
ZHANG Xiaoying;WU Hongqiang;SHU Zijiang;WANG Kun;CHEN Wei(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050;Measurement Department of Marketing Service Center,State Grid Gansu Electric Power Company,Lanzhou 730000)
出处
《电气工程学报》
CSCD
2023年第1期201-210,共10页
Journal of Electrical Engineering
基金
国家自然科学基金资助项目(51867015)。
关键词
双馈风电机组
主成分分析法
人工蜂群算法
主导变量
等值建模
Doubly-fed wind generators(DFWGs)
principal component analysis
artificial bee colony
dominant variable
equivalent modeling
