Data-Driven Sparse Model Identification of Inverter-Based Resources for Control in Smart Grids

With the growing complexity of inverter-dominated grids, there is an emerging need for developing effective modeling tools to identify the dynamics of inverter-based resources (IBRs) interconnected to the grid. This paper utilizes a data-driven approach for identifying the dynamics of grid-tie IBRs in modern power systems. By leveraging the available measurements of the grid-tie IBR and estimation of derivatives of the states, sparse identification of nonlinear dynamics (SINDy) is utilized to obtain the IBR dynamics by selecting a library of candidate functions. The obtained data-driven model is then utilized for designing controllers to regulate the active and reactive powers of the grid-tie IBR. Time-domain simulations validate the effectiveness of the proposed data-driven model identification approach for control purposes in smart grids.