Publications

Resilience Enhancements through Deep Learning Yields

Eydenberg, Michael S.; Batsch-Smith, Lisa B.; Bice, Charles T.; Blakely, Logan; Bynum, Michael L.; Boukouvala, Fani B.; Castillo, Anya C.; Haddad, Joshua H.; Hart, William E.; Jalving, Jordan H.; Kilwein, Zachary A.; Laird, Carl D.; Skolfield, Joshua K.

This report documents the Resilience Enhancements through Deep Learning Yields (REDLY) project, a three-year effort to improve electrical grid resilience by developing scalable methods for system operators to protect the grid against threats leading to interrupted service or physical damage. The computational complexity and uncertain nature of current real-world contingency analysis presents significant barriers to automated, real-time monitoring. While there has been a significant push to explore the use of accurate, high-performance machine learning (ML) model surrogates to address this gap, their reliability is unclear when deployed in high-consequence applications such as power grid systems. Contemporary optimization techniques used to validate surrogate performance can exploit ML model prediction errors, which necessitates the verification of worst-case performance for the models.