Parameter identification and verification of doubly fed induction generator controllers based on Arctic puffin optimization

• Novel APO algorithm for DFIG parameter identification. • Superior global optimization vs. PSO/ACO/MLNN. • Practical hybrid AC/DC microgrid application. In view of challenges faced by the traditional parameter identification methods of complex, nonlinear, multiparameter, and strongly coupled doubly fed induction generators (DFIGs), this paper proposes a parameter identification method based on the Arctic puffin optimization algorithm. The system structure and model of the rotor-side converter of a DFIG is introduced, and a mathematical model of the relationship between the power outer loop of the rotor-side converter and the rotor current is derived. The accuracy of parameter identification is verified via a mutual information (MI) analysis based on waveform data. Finally, measured data of the AC/DC hybrid microgrid system is used for parameter identification, further confirming the applicability and reliability of the algorithm in engineering applications.