To address the impact of unknown parameter uncertainties and correlations on the identification of doubly-fed induction generator-based wind turbine dynamic model mechanical parameters

this paper first proposes an integrated parameter identifiability analysis method. This method offers precise guidance for selecting disturbance excitations

observation variables

and identifiable parameters in complex model identification. Building on this analysis

a stepwise decoupling identification strategy is introduced for mechanical parameters

leveraging differences in parameter identifiability across operational scenarios to mitigate the effects of implicit parameter correlations. Within each identification step

mechanical parameters are repeatedly estimated under randomly assigned values of low-sensitivity parameters

such as those in electrical and controller models. Central values of high-probability samples are then obtained through iterative application of the K-medoids

effectively reducing the influence of inaccurate low-sensitivity parameters. The effectiveness of the proposed methodology is validated through simulation studies.