Grid monitoring with synchro-waveform and AI foundation model technologies

This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources based on high resolution sychro-waveform measurement technology. Leveraging recent progress in generative Artificial Intelligence (AI), machine learning, and networking technology, we develop a physics-based AI foundation model to enhance grid resilience and reduce economic losses from outages. The proposed framework adopts the AI Foundation Model paradigm, where a Generative and Pre-Trained (GPT) foundation model extracts physical features from power system measurements, enabling adaptation to a wide range of grid operation tasks. Replacing the large language models used in popular AI foundation models, this approach is based on the Wiener–Kallianpur–Rosenblatt innovation model for time series, trained to capture the physical laws of power flows and sinusoidal characteristics of grid measurements. The pre-trained foundation model causally extracts sufficient statistics from grid measurement time series for various downstream applications, including anomaly detection, over-current protection, probabilistic forecasting, and data compression for streaming synchro-waveform data. Numerical simulations using field-collected data demonstrate significantly improved fault detection accuracy and detection speed. The future grid will be rich in inverter-based resources, making it highly dynamic, stochastic, and low inertia.