基于Crowbar保护的三相短路电流的DFIG参数辨识方法
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  • 英文篇名:New Parameter Identification Method of DFIG with Three-phase Short Circuit Current Based on Crowbar Protection
  • 作者:朱玺文 ; 张宏宇 ; 缪辉 ; 黄帅飞
  • 英文作者:ZHU Xiwen;ZHANG Hongyu;MIAO Hui;HUANG Shuaifei;College of Energy and Electrical Engineering,Hohai University;State Grid Anhui Chuzhou Power Supply Company;
  • 关键词:DFIG ; Crowbar保护 ; 三相短路电流 ; 轨迹灵敏度 ; 参数辨识
  • 英文关键词:DFIG;;Crowbar protection;;three-phase short-circuit current;;trajectory sensitivity;;parameter identification
  • 中文刊名:XBDJ
  • 英文刊名:Smart Power
  • 机构:河海大学能源与电气学院;国网安徽省电力公司滁州供电公司;
  • 出版日期:2019-06-20
  • 出版单位:智慧电力
  • 年:2019
  • 期:v.47;No.308
  • 基金:国家自然科学基金资助项目(51707056);; 中央高校基本科研业务费专项资金资助(2017816714)~~
  • 语种:中文;
  • 页:XBDJ201906019
  • 页数:6
  • CN:06
  • ISSN:61-1512/TM
  • 分类号:127-132
摘要
双馈风力发电机(DFIG)作为风电机组的核心部件,其准确的模型参数对于含风电电力系统的动态安全分析与控制至关重要。提出了一种在风机端口三相短路故障下,利用计及Crowbar保护动作的风机短路电流辨识DFIG参数的方法。在DFIG传统三相短路电流解析模型的基础上,考虑定转子磁链之间的耦合以及Crowbar电阻对故障暂态过程的影响,提出了一种计及Crowbar保护的DFIG三相短路电流解析式。采用轨迹灵敏度法分析DFIG参数的可辨识性以及辨识难易度。基于Matlab/simulink仿真平台搭建DFIG风电场模型获取短路电流数据,采用自适应变异粒子群(AMPSO)算法辨识DFIG模型参数,并对辨识结果进行误差分析,仿真结果验证了所提方法的正确性。
        Doubly-fed induction generator(DFIG)is the core component of wind turbine,its accurate mode parameters are essential for dynamic safety analysis and control of wind power systems.A method is proposed to identify the DFIG parameters by using the shortcircuit current considering Crowbar protection under the three-phase short-circuit fault of the wind turbine port.Based on the DFIG traditional three-phase short circuit current analysis model,considering the coupling between stator and rotor flux and the influence of Crowbar resistance on the fault transient process,a DFIG three-phase short circuit current analytical formula considering Crowbar protection is proposed.Trajectory sensitivity method is used to analyze the identifiability and difficulty of DFIG parameters.The shortcircuit current data obtained from the Matlab/Simulink wind farm model are used to identify the DFIG parameters by the adaptive variable particle swarm(AMPSO)optimization algorithm,and the error analysis of the identification results is carried out to verify the correctness of the proposed method.
引文
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