The inherent modelling of the operational wind turbines and rotating machines do not agree in general with the assumptions of the operational modal analysis (OMA) methods developed for civil engineering, where time invariant systems are considered. Current OMA methods for rotating machines introduce datapre-processing to adapt classical identification methods. However, they show strong limitations and rely on strong assumptions, such as the isotropy of the rotor, making them hardly applicable in practice. To overcome these limitations, this paper proposes to employ the Floquet theory of periodic system to approximate rotating systems as time invariant systems. Thus, classical identification methods can be used to retrieve the parametric signature of the periodic systems. This Floquet-based approximation gives a physical meaning to the identified eigenmodes. The proposed approach is validated on both a small numerical model and an aero-servo-elastic numerical model of a rotating 10MW wind turbine, with isotropic and anisotropic rotors, using the stochastic subspace identification to retrieve the modes and their uncertainty.