摘要
针对运行过程中双馈风机变流器控制参数难以获取的问题,提出了一种基于自适应混沌粒子群算法的转子侧变流器参数辨识方法。首先,基于机组实际运行下可量测电气量时间序列,建立双馈风机变流器控制系统离散化数学模型;然后,根据不同观测电气量下参数的轨迹灵敏度,对辨识难易程度进行分析;最后,利用自适应混沌粒子群算法对变流器PI控制参数进行辨识。仿真实验结果验证了所提出辨识方法的准确性与可行性。
In order to solve the problem that the control parameters of doubly-fed fan converter are difficult to be obtained,an adaptive chaotic particle swarm optimization algorithm is proposed to identify the parameters of the rotor-side converter.Firstly,based on the time series of the measurable electrical quantities in the actual operation of the unit,the discrete mathematical model of the control system of the doubly fed fan converter is established.Then,the difficulty of identification is analyzed according to trajectory sensitivity of parameters under different observable electrical information.Finally,the PI control parameters of the converter are identified by the adaptive chaotic particle swarm optimization(APCPSO)algorithm.The simulation results verify the accuracy and feasibility of the proposed identification method.
作者
董福杰
刘颖明
王晓东
赵宇
王宇
DONG Fujie;LIU Yingming;WANG Xiaodong;ZHAO Yu;WANG Yu(School of Electrical Engineering,Shenyang University of Technology,Shenyang 110870,China)
出处
《电力科学与工程》
2024年第3期61-69,共9页
Electric Power Science and Engineering
基金
辽宁省揭榜挂帅科技攻关专项基金资助项目(2021JH1/10400009)。
关键词
风力发电机组
参数辨识
转子侧变流器
自适应混沌粒子群算法
wind turbine
parameter identification
rotor-side converter
adaptive chaotic particle swarm optimization algorithm
