摘要
目前双馈式风力发电机(DFIG)为国内外风力发电机的主流机型。要研究大规模风电并网对电力系统可靠性和稳定性等方面产生的影响,必须要有准确的风力发电机的模型和参数。研究了双馈风力发电机的模型参数辨识方法。首先在Matlab/Simulink环境中搭建了风力发电机并网的仿真模型,得到并网的实测数据。然后选用发电机的αβ坐标系数学模型,在对模型进行可辨识性分析的基础上得到定子自感、定转子互感的表达式。最后利用遗传算法,并采用分步辨识策略,进一步辨识出转子初相位θ0角、定子自感、定转子互感、转子互感等风力发电机参数。为研究大规模风电并网等课题提供了可靠的理论依据。
Doubly-fed wind power generator （DFIG） type is the mainstream model of the wind turbine at home and abroad at present. To study the impact on reliability and stability of power system with large-scale wind power grid, it must have accurate model and parameters of wind turbine. Therefore, this paper studies the model parameter identification method of doubly-fed wind power generator. First, in the Matlab/Simulink environment, it sets up a simulation model for the wind turbine grid-connected and gets the measured data. Afterwards, by using the generator αβ coordinate system （mathematical） model, it analyzes the identifiability to illustrate the convergence for the proposed model, and then through the analysis of the mathematical model, gets an expression of stator self-induction and the mutual inductance. Finally, the rotor initial phase angleθ , stator self inductance, mutual inductance, and 0 mutual inductance between rotor and stator can be identified by using genetic algorithm with identification strategy step by step. It provides reliable theory for the study of large-scale wind power grid.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2014年第20期81-85,共5页
Power System Protection and Control
基金
中国南方电网公司科技项目(K-YN2012-018)
关键词
双馈风力发电机
参数辨识
遗传算法
doubly-fed wind power generator
parameter identification
genetic algorithm
