Data-Driven Models for Sub-Cycle Dynamic Response of Inverter-Based Resources Using WMU Measurements.

Using real-world data from Waveform Measurement Units (WMUs), this letter proposes novel data-driven methods to model the dynamic response of inverter-based resource (IBR) to the high-frequency disturbances that occur in practice in power systems. WMUs are an emerging class of smart grid sensors. They can capture the fast sub-cycle dynamics in power systems, which are overlooked by phasor measurement units (PMUs). After extracting the differential voltage and current waveforms from the raw waveform data, we develop multiple methods that include data-driven model library construction and proper model selection. One class of methods is proposed in frequency domain, which is based on modal analysis. Another class of methods is proposed in time domain, which is based on regression analysis of time-series. Experimental results based on real-world WMU data demonstrate the of performance the proposed methods.