AMPS: Dynamics-Aware Algorithms for Real-Time Structured Fault Detection in Power Systems

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The U.S. power grid is in the midst of its most fundamental transformation since its inception. Spurred by the need to reduce emissions, the electric generation mix is drifting away from traditional, reliable sources, towards volatile and uncertain renewable sources. Simultaneously, there is an unprecedented increase in the quantity, quality, and variety of sensing and monitoring devices. From phasor measurement units (PMUs) to smart meters, the power grid is soon to experience an overflow of data that has the potential of providing an extraordinary amount of information at the transmission and distribution levels. However, despite this burst in data availability, there is still a lack of analytic tools that can leverage the newly available telemetry to help operators face the paradigm shift that renewable sources pose. Moreover, the modernization of monitoring systems that use cyber resources to transmit information for processing and analysis begets new challenges and threats. Without the proper tools to correct and validate the collected data, undetected errors or maliciously modified data can mislead operators and bring the system towards blackouts. This work addresses these challenges by developing novel algorithmic tools that take into account intrinsic properties of the measurements.This project develops a theoretical framework and associated algorithms that can reliably utilize the newly available measurements to provide useful real-time information that can allow operators to use resources better and react to unforeseen events. More precisely, the PIs seek to combine tools from statistics, dynamical systems, and optimization to develop a data analytic approach to identify and prevent cyber-physical attacks, correct missing, and corrupted data, identify network structural changes, and recognize abrupt local changes in supply- demand imbalance. This research is unique within the existing the literature in power system monitoring in several ways. Firstly, it acknowledges and leverages the fact that there are spatial and temporal correlations between the measurements generated by the underlying dynamical system (the power grid). Secondly, it builds a unifying modeling framework that can jointly capture how grid measurements are affected by (a) topology changes in the network, (b) abrupt changes in supply or demand, and (c) measurement errors. Thirdly, it develops a novel algorithmic framework that exploits structural sparsity properties of the different network disturbances to discriminate and identify the source of a given grid transient behavior. The research will also build a large-scale simulation testbed to assess the accuracy and scalability of the designed algorithms and in this way bridge the gap between theoretical models and actual power systems.