Collaborative Research: High-Dimensional Spatio-Temporal Data Science for a Resilient Power Grid: Towards Real-Time Integration of Synchrophasor Data

点击翻译按钮获取中文摘要

The project will establish an Institute at Arizona State University (ASU) with Texas A&M (TAMU) that considers the electric power grid and examines critical real-time decision-making by developing core data-driven science methods and applications. This is motivated by the modern electric power system which is experiencing heightened unpredictability from increasing demand for renewable energy, efficiency, and resilience. To address this, industry stakeholders are deploying GPS-synchronized phasor measurement units (PMUs), or synchrophasors, that provide direct measurements of voltage and current phasors with high temporal granularity. However, the potential real-time situational awareness enabled by these measurements has been impeded by the massive scale of the time-series PMU data and have limited its use to passive, post-event forensics. The Institute meets this need for PMU-based real-time decision-making by examining five critical problems: (i) ensure data quality against bad, missing, or stale data; (ii) exploit the fine granularity of PMU data to track real-time changes in network parameters; (iii) detect, identify, localize, and visualize oscillation and failure events; (iv) assess and visualize cybersecurity threats and countermeasures specific to PMUs; and (v) create synthetic PMU datasets for testing and validation. The Institute leverages the PIs' synergistic multidisciplinary background in information sciences and statistics, machine learning, data visualization, cybersecurity, and power systems. The team will apply state-of-the-art techniques including hidden Markov models, LSTM neural networks, graphical models, errors-in-variables models, graph signal processing, adversarial examples, low-dimensional feature extraction, and constrained GANs. Another key research focus is the development of visual analytics for high-granularity spatio-temporal PMU data to enable improved operator review and decision-making. These innovations will be fueled by massive PMU datasets accessible to the PIs.This Phase I institute has the potential to tip PMUs from a promising-but-mostly-underused resource into an essential part of power system best practices. The data science outcomes will impact application domains such as transportation networks, smart buildings, and manufacturing, each of which increasingly faces high-dimensional streaming data challenges. The PIs will disseminate their research to both academic and industry stakeholders and will continue their outreach on teaching AI and machine learning (ML) modules to underrepresented high school students. Finally, the multi-disciplinary strength of this institute lends itself naturally to a larger, integrated, and comprehensive Phase II institute focused on data-intensive research for critical infrastructure networks.This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity. This effort is co-funded by the Division of Electrical, Communications and Cyber Systems within the Directorate for Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.