Data-Centric Theories and Singular Value Decomposition (SVD) for Identification of Inverter-Based Resources

This paper presents a pioneering data-centric approach for model identification of inverter-based resources (IBRs) in smart grids, which includes renewable energy systems and energy storage technologies. Unlike traditional methods that depend on fixed models and extensive system identification tools, our approach leverages emerging systems behavioral theories and combines them with singular value decomposition (SVD) to efficiently identify IBR models from data. The SVD’s ability to reduce the dimension of collected data is instrumental in capturing critical dynamic features from minimal data inputs. By applying the principle of persistence of excitation and organizing input/output data into a Hankel matrix form, we derive a robust, model-free representation of IBR dynamics that requires significantly less data than conventional machine learning methods. The effectiveness of our approach is validated through comprehensive time-domain simulations, demonstrating its potential for model- free IBR control applications.