Identification of Electrical Parameters of Doubly-fed Wind Turbine Based on Gray Wolf Optimization

Aiming at the problem of insufficient accuracy of electrical parameter identification in the low-voltage ride-through (LVRT) process of Doubly fed Induction Generator (DFIG), this paper proposes an electromechanical transient parameter identification method based on the Gray Wolf Optimization (GWO) algorithm. By establishing a simplified model of DFIG, the rotor resistance and stator resistance are taken as the parameters to be identified, and the global optimization of parameters is realized by using the group intelligent optimization characteristic of GWO algorithm. Based on the measured data of 0.2pu and 0.75pu voltage dips in a wind farm in Guangxi, the PSD-BPA simulation validation shows that the root mean square error (RMSE) of the voltage, active and reactive power responses of this method in the pre-fault, mid-fault, and post-fault phases is less than 0.02, and the results of this study provide a feasible parameter identification scheme for the calibration of DFIG grid-connected model and the evaluation of LVRT capability.