Beyond Phasors: Synchro-Waveforms and Neural Representations for Power System Monitoring and Situational Awareness

Recent advancements in power system measurement technologies are enabling a shift in grid analytics from phasor-based representations to more authentic and granular time-domain waveform data. When combined with precise time synchronization across multiple locations, these measurements (referred to as synchro-waveforms) provide access to time-aligned, system-wide voltage and current signals at high sampling rates, capturing fast transients and dynamic behaviors that are not visible in traditional approaches, particularly in systems with emerging large loads such as AI training data centers, as well as increasing penetration of inverter-based resources. While these developments significantly increase data availability, they also introduce new challenges in how waveform data are represented, stored, and analyzed. This talk highlights emerging approaches to waveform-level analytics, with a focus on implicit neural representations (INRs) as a continuous and compact modeling framework for voltage and current waveforms. The presentation illustrates how INR-based models can capture both periodic structures and transient dynamics while enabling efficient representation of multi-phase and time-synchronized measurements. The talk also provides a perspective on real-world applications of waveform and synchro-waveform analytics in power systems.