A Data-model-driven Modeling Approach for Grid Connected Doubly Fed Induction Generator System Using Trajectory Piecewise Linearization

Wind farm integrated system has been an effective method of delivering renewable energy. However, owing to the stochastic operating mode and disturbances applied to the system, it is essential to know the operating trajectory properties after disturbances. Therefore, this paper implements a data-model-driven modeling approach to develop an order-reduced model appropriate for capturing the nonlinear properties of the operating trajectory after disturbance. Firstly, the modeling of a grid-connected doubly fed induction generator is built in great detail. And then, the method based on trajectory piecewise linearization generates three trajectories from a specific steady operation point with three disturbances to build a reduced-order model. Comparing the original, linearized model and the reduced order model’s simulation results demonstrate that the approach can capture the nonlinear system’s transient dynamics accurately and effectively. This will be beneficial for future controller design under different operating points.