Control parameter identification of doubly-fed wind turbine based on improved black hole algorithm

To address the issues of inadequate speed and accuracy in parameter identification, as well as the excessive number of parameters associated with existing doubly fed induction generators (DFIG), a control parameter identification method based on the improved black hole algorithm (IBHA) is proposed. Firstly, the control equations and control model of the rotor-side converter of DFIG are established; secondly, the effects of different control parameters on the characteristics of the wind turbine response curve are different, for which their trajectory sensitivities are calculated, the optimal observation quantities are determined, and the identifiability of the parameters is analyzed; thirdly, the black-hole algorithm (BHA) is improved by using the mechanism of chaotic mapping and other mechanisms, and the control parameters are identified through the IBHA; Finally, the identification results of particle swarm optimization (PSO),BHA and IBHA are compared and analyzed, and the results show that compared with these two traditional heuristic algorithms, IBHA identification method proposed in this paper can effectively improve the speed and accuracy of control parameter identification of DFIG.