Data driven techniques in power systems
Leverage machine learning and data-driven approaches for uses in power systems, such as time-domain simulation acceleration, surrogates and frequency/voltage regulation.
Publications
Learning to control in power systems: Design and analysis guidelines for concrete safety problems.
Roel Dobbe, Patricia Hidalgo-Gonzalez, Stavros Karagiannopoulos, Rodrigo Henriquez-Auba, Gabriela Hug, Duncan S Callaway, Claire J Tomlin
Electric Power Systems Research, vo. 189, pp. 106615, 2020.
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Frequency Regulation using Sparse Learned Controllers in Power Grids with Variable Inertia due to Renewable Energy.
Patricia Hidalgo-Gonzalez, Rodrigo Henriquez-Auba, Duncan S Callaway, Claire J Tomlin
IEEE 58th Conference on Decision and Control (CDC), pp. 3253-3259, 2019.
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Frequency regulation using data-driven controllers in power grids with variable inertia due to renewable energy.
Patricia Hidalgo-Gonzalez, Rodrigo Henriquez-Auba, Duncan S Callaway, Claire J Tomlin
IEEE Power and Energy Society General Meeting (PESGM), pp. 1-5, 2019.
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Frequency Regulation in Hybrid Power Dynamics with Variable and Low Inertia due to Renewable Energy.
Patricia Hidalgo-Gonzalez, Duncan S Callaway, Roel Dobbe, Rodrigo Henriquez-Auba, Claire J Tomlin
Conference on Decision and Control (CDC), pp. 1592-1597, 2018.
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