中国电力2024，Vol.57Issue(8)：75-84,10.DOI:10.11930/j.issn.1004-9649.202310040
基于混沌粒子群的双馈风电机组LVRT实测建模及暂态参数辨识
LVRT Measurement Model and Transient Parameter Identification of Wind Turbine Based on Chaotic Particle Swarm
摘要
Abstract
The high-accuracy simulation model is the basis for transient stability analysis of large-scale wind power integration.However,the control strategies and parameters of doubly-fed wind turbines are technical secrets that are difficult to obtain,and the accuracy of model simulation is difficult to guarantee.In order to address the fault transient modeling problems of doubly-fed wind turbines,a measured data-based modeling and parameter identification method of doubly-fed wind turbines is proposed.Firstly,based on the DFIG model and control structure of the Power System Integrated Stability Program(PSASP),a low voltage ride through(LVRT)control mathematical model is established to analyze the fault transient process,and the LVRT transient control core parameters are clarified.Secondly,based on part of the field measured LVRT data of doubly-fed wind turbines,the fault transient parameters are identified with the chaotic particle swarm optimization algorithm.Finally,the accuracy of the identification parameters are analyzed and verified based on the remaining measured data.The simulation results have verified the effectiveness and accuracy of the proposed parameter identification method.The proposed method has strong generalization ability and high accuracy of identification results,and is of great engineering application value.关键词
双馈风电机组/低压穿越/参数辨识/实测数据/混沌粒子群Key words
double-fed induction generator/low voltage ride through/parameter identification/measured data/chaotic particle swarm引用本文复制引用
李丹,秦世耀,李少林,贺敬..基于混沌粒子群的双馈风电机组LVRT实测建模及暂态参数辨识[J].中国电力,2024,57(8):75-84,10.基金项目
国家电网有限公司科技项目(新能源电站的实测建模与模型参数优化技术研究,5100-202155481A-0-5-ZN).This work is supported by Science and Technology Project of SGCC(Modeling and Parameters Optimization Technologies Based on Testing Data of Renewable Energy Power Station,No.5100-202155481A-0-5-ZN). （新能源电站的实测建模与模型参数优化技术研究,5100-202155481A-0-5-ZN）
