Machine Learning in Power System Operations: Training Data

Bukowski, Stephen Arthur; Sticht, Christopher Roger

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DOE OSTI · 2007447

Machine Learning in Power System Operations: Training Data

Bukowski, Stephen Arthur·Sticht, Christopher Roger

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Abstract

Reliability and stability of the electric grid today has depended upon operations of the grid which include the protective relay. Today, the electricity sector faces new challenges with the shift of generation resource characteristics away from the traditional “big iron” generation to inverter-based resources (IBR) which shift the physics and assumption used in grid operation and protection. These changing conditions represent new challenges for protective relays (identification of faults) and increased challenges for protection engineers (correct settings and configuration, reduction of mis-operations), both issues recognized in research and industry. Finding new approaches to reduce mis-operations in relaying and new approaches to fault identification is critical to grid operations. Using today’s modern technology of embedded systems, edge computing, machine learning (ML), and communications we can help address challenges and augment and improve on existing power system operations methodologies.

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Bukowski, Stephen Arthur, Sticht, Christopher Roger. 2023-07-17. Machine Learning in Power System Operations: Training Data. https://www.osti.gov/biblio/2007447

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