Multistep Identification of the DFIG Model Based on Dynamic Analysis of Parameter Identifiability

Accurate model parameters are crucial for power system simulation analysis and control design. However, it is challenging to identify complex models owing to the implicit correlation between parameters and low-rank identification equations. Given this, a parameter pre-estimation technique establishes quantitative mappings between multi-parameter variations and observable dynamics to obtain suitable parameter optimization ranges. The parameter identifiability indices are constructed to quantitatively assess the identifiability of model parameters under various scenes to formulate the feasible parameter identification strategy. A multistep parameter identification method combining the dynamic analysis of parameter identifiability is proposed to solve the model underranking problem and improve the overall identification accuracy of model parameters. Finally, the effectiveness of the proposed method is illustrated by simulation experiments.