Observability analysis and state estimation of wind turbine power systems: A novel sensitivity-based approach

In this paper, we provide a novel framework that enables a sensitivity-based observability test and state estimation algorithm for wind turbine power systems (WTPSs). The provided framework is the first of its kind in the literature, as it is able to deal with state-of-the-art WTPS models that are non-reduced, highly nonlinear differential–algebraic equation systems. Moreover, the framework includes nonsmoothness in both the dynamics and output functions to unify the operational conditions over different wind speed regions. We demonstrate the effectiveness of the proposed framework (thanks to the underlying tools from generalized derivatives theory) on different wind speed profiles, including real-world wind data. We also illustrate how the proposed framework, by the utilization of robust observability analysis during nonsmooth transitions, enables accurate state estimation for cases when the conventional Extended Kalman Filter approach fails.