Data-Driven Modeling and Prediction of Transient Dynamics of Microgrids through Dynamic Mode Decomposition with Control

Dynamic Mode Decomposition with Control (DMDc) is presented to develop a data-driven modeling approach for microgrids when the accurate mathematical model is unavailable. As a system identification method, it can precisely model and predict the transient response of microgrids subject to large disturbances. A modified DMDc method is introduced to handle the piecewise constant inputs issue. Precautions on applying the method on measurement data are also discussed. Numerical examples on a typical islanded microgrid have demonstrated that local dynamics can be accurately captured through the DMDc-based data-driven model. By requesting a small amount of data, the data-driven modeling is powerful for performing system forecast and data-driven control.