Parameter Identification and Stability Analysis of DFIG

Abstract

A Doubly-Fed Induction Generator (DFIG) through the converter connected to the grid could cause subsynchronous oscillation. However, it is difficult to obtain control parameters from the manufacturer of the converter, making it challenge to assess stability issues. In this paper, a method based on machine learning algorithm to analyze grey box DFIG’s grid stability is proposed. Accurate identification of the inner loop and outer loop parameters on the rotor side converter is achieved. This method can solve the problem of identifying low sensitivity parameters. Parameter identification and stability analysis is based on model-driven and data-driven approach. The impedance model of grey box is firstly established, and then the control parameters are identified by machine learning technique. Finally, the system stability of DFIG is analyzed by generalized Nyquist. A simulation based on MATLAB/Simulink is provided to validate the performance of the proposed strategy.