The invention discloses a doubly-fed
induction motor parameter identification method based on adaptive extended Kalman filtering, and aims to solve the problems of low doubly-fed
induction motor parameter identification precision and integrity, sensitive
noise and the like in the prior art. According to a dq-axis
flux linkage equation and a
voltage equation of the doubly-fed
induction motor, a
state space model with dq-axis current of a
stator and a rotor as state variables is built, the dimension of the
state space model is expanded, the full-parameter
observability of resistance Rs and Rr and
inductance Ls, Lr and Lm of the motor is judged by utilizing a self-adaptive extended Kalman
filtering theory and combining a rank criterion, and the accuracy and accuracy of the
state space model are improved. All-parameter identification of
inductance and resistance of the motor is realized, and the robustness of the
system is improved through a method of adaptively adjusting
noise covariance. The parameter
coupling influence can be effectively eliminated, and the identification precision is improved; the self-adaptive
noise suppression mechanism enhances the anti-interference capability, and meets the dynamic on-line parameter identification requirements of the doubly-fed motor.