Hierarchical progressive identification strategy for DFIG parameters based on multi-timescale fault process partitioning

To address the parameter identification problem of black-box models for doubly-fed induction generator (DFIG) wind turbines under multiple operating conditions, this paper proposes a hierarchical progressive parameter identification strategy based on the partitioning of multi-timescale fault process. Firstly, the model structure and parameters to be identified are determined according to the dynamic response characteristics of the black-box model. Subsequently, the sensitivity of parameters across different time scales is quantitatively analyzed using perturbation theory, and a hierarchical progressive identification method is established according to the dominant parameter response characteristics in different operational stages. Furthermore, by leveraging the differential response characteristics of parameters across hierarchical levels, the differential evolution method is adopted to realize adaptive identification of multiple parameters. Finally, a white-box model parameter identification method applicable to various manufacturers and models is developed. Comparative results show that the proposed hierarchical progressive identification strategy has good applicability and robustness under different operating conditions and for different models. Additionally, comparisons results with traditional parameter identification methods also demonstrate that the proposed approach exhibits superior rapidity and accuracy.