Improving Grid Awareness by Empowering Utilities with Machine Learning and Artificial Intelligence

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This project uses artificial intelligence and machine learning methods to provide grid operators and engineers with real-time analysis and visualization capabilities of the electric power system. Cloud computing approaches to system monitoring and real-time analytics provide a model for leveraging multiple data sources to correlate, verify, and interpret system telemetry in environments with high scale and low data fidelity. Experience from systems design in related fields shows that in sufficiently complex systems, no single data source can be entirely accurate or trustworthy, but an approach that leverages multiple sources and applies intelligent data interpretation can provide an extremely reliable, high-fidelity systems view. This project leverages the team's past experience with cloud systems monitoring approaches and abundant data for artificial intelligence model training, along with capabilities in integrated power system simulation and monitoring data analytics with machine learning and deep learning to provide advanced, integrated situational awareness for the distribution grid and contributions to area-wide flexibility.

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