Assistant Professor

Samuel Chevalier is an Assistant Professor in the Department of Electrical and Biomedical Engineering at ¶¶Òõ̽̽. His research interests include designing industry-relevant optimization and control strategies for renewable-based power grids, building trustworthy machine learning tools for safety-critical engineering applications, and developing advanced data-driven modeling techniques for the power and energy sectors. Prior to joining ¶¶Òõ̽̽, Sam was a Marie SkÅ‚odowska-Curie Postdoctoral Follow at the Technical University of Denmark, where his research focused on developing computational techniques for verifying the performance of Neural Network models of electrical power systems. Sam received his PhD from the Mechanical Engineering department at the Massachusetts Institute of Technology (MIT) in 2021, and he received his BS/MS degrees from the EE department at ¶¶Òõ̽̽ in 2015/2016. Sam is a 6th generational Vermonter, and when he isn’t working on power systems, he is exploring Vermont’s beautiful nature with his wife and daughter. 

Publications

- Kody, A., Chevalier, S., Chatzivasileiadis, S., & Molzahn, D. (2022). Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment. Electric Power Systems Research, 213, 108282.

- Chevalier, S., & Almassalkhi, M. R. (2022, December). Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid. In 2022 IEEE 61st Conference on Decision and Control (CDC) (pp. 5713-5718). IEEE.

- Chevalier, S., Schenato, L., & Daniel, L. (2021). Accelerated probabilistic power flow in electrical distribution networks via model order reduction and neumann series expansion. IEEE Transactions on Power Systems, 37(3), 2151-2163.

- Chevalier, S., Vorobev, P., & Turitsyn, K. (2020). A passivity interpretation of energy-based forced oscillation source location methods. IEEE Transactions on Power Systems, 35(5), 3588-3602.

Assistant Professor Sam Chevalier

Areas of Expertise and/or Research

Electrical Power Systems, Machine Learning, Network Optimization

Education

  • Ph.D., Massachusetts Institute of Technology; -M.S., Univeristy of Vermont; - B.S., University of Vermont

Contact

Email:

schevali@uvm.edu

Phone:
  • 802-777-9708
Office Location:

Billings 158B

Website(s):

Courses Taught

EE5310 - Power System Analysis