To address the issues of low efficiency, limited precision, and multiple parameters in existing PI controller identification methods for rotor-side converters （RSC） of doubly-fed induction generators （DFIG）, a hierarchical identification framework combining an improved black hole algorithm （IBHA） with the RT-LAB hardware-in-the-loop （HIL） co-simulation platform is proposed. Firstly, based on the RSC control dynamics, the operational characteristic data of the actual controller of DFIG is collected by the RT-LAB HIL co-simulation platform. Secondly, a trajectory sensitivity analysis is performed to quantify the dynamic response characteristics of each parameter, followed by the selection of optimal observables and a rigorous identifiability assessment based on persistent excitation criteria. Thirdly, a stepwise identification strategy is established based on the RT-LAB measured data and IBHA. Finally, based on the HIL test data, the identification results of several algorithms are compared and analyzed, which verifies the effectiveness of the proposed method.

wind turbine generators  /  parameter identification  /  sensitivity analysis  /  hardware-in-the-loop simulation  /  black hole algorithm  /  voltage ride-through experiment