基于PMU实测数据的DFIG风电场等值模型鲁棒性与适应性分析  

ROBUSTNESS AND ADAPTABILITY ANALYSIS OF EQUIVALENT MODEL OF DFIG WIND FARM BASED ON MEASURED DATA OF PMU

作　　者：张剑[1] 崔明建 何怡刚 Zhang Jian;Cui Mingjian;He Yigang(School of Electric and Automation Engineering,Hefei University of Technology,Hefei 230009,China;School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China;School of Electric and Automation Engineering,Wuhan University,Wuhan 430072,China)

机构地区：[1]合肥工业大学电气与自动化工程学院,合肥230009 [2]天津大学电气自动化与信息工程学院,天津300072 [3]武汉大学电气与自动化学院,武汉430072

出　　处：《太阳能学报》2023年第10期320-328,共9页Acta Energiae Solaris Sinica

基　　金：国家自然科学基金(52207130)。

摘　　要：对传统聚合方法无法解决风电场长期运行过程中产生的参数变化问题,该文基于实测数据建立双馈感应发电机(DFIG)风电场详细等值模型与初始化方法,分析时变参数的轨迹灵敏度,提出首先将非时变参数固定为聚合值,然后利用风电场公共并网点相量测量单元(PMU)数据针对时变参数采用基因学习粒子群(GLPSO)混合算法进行参数辨识的策略。采用WECC标准算例分析不同风速、尾流效应、部分风电机组离线、风速未知、不同短路故障位置与电压暂降深度情形下等值模型的鲁棒性与适应性。仿真算例表明所提出的参数辨识方法全局寻优能力远高于标准粒子群与遗传算法。而且,对于高灵敏度参数,参数辨识结果与真实值的最大偏差小于10%,远优于目前技术水平。In this paper,in view of the fact that the traditional aggregation method cannot solve the problem of parameters variation after long-term operation of wind farms,a detailed equivalent model of Doubly Fed Induction Generators(DFIGs)wind farm and initialization method are developed.The trajectory sensitivity of parameters is analyzed.Parameters identification strategy is proposed that the non-time-varying parameters are fixed as aggregated values,while the Genetic Learning Particle Swarm Optimization(GLPSO)hybrid algorithm is used to identify time-varying parameters based on Phasor Measurement Unit(PMU)data at the common interconnection point of wind farm.The robustness and adaptability of the equivalent model of DFIG wind farm under different wind speeds,wake effects,unknown wind speed,different short-circuit fault locations and voltage sags depth and some DFIGs off-line are analyzed.The simulation results using the Western Electricity Coordinating Council benchmark test system show that the global searching capability to find the optimal solution of the proposed method is much higher than that of canonical particle swarm optimization(PSO)and genetic algorithm(GA).Further,the maximum deriation between the identification results using the proposed method and the true values is less than 10%with high sensitivity parameters,which is much better than previous state-of-art work.

关 键 词：双馈风力机 风电场等值模型 参数辨识 轨迹灵敏度 配网阻抗 电力系统 

分 类 号：TM614[电气工程—电力系统及自动化]