Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies

• URL: http://arxiv.org/abs/2403.06942v2
• Date: Sat, 25 Jan 2025 17:57:12 GMT
• Title: Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies
• Authors: Lang Tong, Xinyi Wang, Qing Zhao,
• Abstract summary: This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. We develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages.
• Score: 41.994460245857404
• License:
• Abstract: Purpose:This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. Leveraging recent progress in generative artificial intelligence (AI), machine learning, and networking technology, we develop a physics-based AI foundation model with high-resolution synchro-waveform measurement technology to enhance grid resilience and reduce economic losses from outages. Methods and Results: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 power system 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. Conclusion:The future grid will be rich in inverter-based resources, making it highly dynamic, stochastic, and low inertia. This work underscores the limitations of existing Supervisory-Control-and-Data-Acquisition and Phasor-Measurement-Unit monitoring systems and advocates for AI-enabled monitoring and control with high-resolution synchro-waveform technology to provide accurate situational awareness, rapid response to faults, and robust network protection.

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