Associate Professor, Electrical Engineering
Dr. Qiuhua Huang received his Ph.D. degree in electrical engineering from Arizona State University in 2016, B.Eng. and M.Eng. degrees in electrical engineering from South China University of Technology, Guangzhou, China, in 2009 and 2012, respectively. Before joining Mines, he was a Principal Power System Engineer at UtiliData Inc, working on grid digitization and edge intelligence. Prior to that, he was a Staff Power System Research Engineer at Pacific Northwest National Laboratory, WA. USA. He is the recipient of the 2019 IEEE Power and Energy Society (PES) Prize Paper Award, 2018 R&D 100 Award and a few best conference paper awards in IEEE PES General Meeting. He serves as an Associate Editor of IEEE Transactions on Power Systems and served as an Editor of CSEE JPES, Guest Editor of IET Generation, Transmission and Distribution and IET Smart Grid. His research interests include power system modeling, simulation and control, fusion and application of AI/machine learning and advanced computing technologies for digitizing and transforming power and energy systems.
- BEng, Electrical Engineering, South China University of Technology, Guangzhou, China, 2009
- MEng, Electrical Engineering, South China University of Technology, Guangzhou, China, 2012
- PhD, Electrical Engineering, Arizona State University, 2016
- Power System Modeling, Simulation and Control
- Fusion and Application of AI/machine learning
- Advanced Computing Technologies for digitizing and transforming power and energy systems
Honors and Awards
- IEEE PES General Meeting Best Conference Paper, 2020
- IEEE PES Prize Paper Award, 2019
- IEEE PES Technical Committee Prize Paper Award, 2019
- Publication Of-The-Year Award, PNNL EED, 2019
- R&D 100 Finalist, 2019
- R&D 100 Award, 2018
- IEEE PES General Meeting Best Conference Paper, 2018
- Transformative remedial action scheme tool (TRAST), US20200387121A1
- R. Huang, Y. Chen, T. Yin, Q. Huang*, J. Tan, W. Yu, X. Li, A. Li, Y. Du, “Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning,” IEEE Trans. on Power Systems, vol. 37, no. 6, pp. 4168-4178, Nov. 2022
- Q. Huang, R. Huang, W. Hao, J. Tan, R. Fan, Z. Huang. “Adaptive Power System Emergency Control Using Deep Reinforcement Learning,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1171-1182, March 2020
- Q. Huang, V. Vittal. “Advanced EMT and phasor domain hybrid simulation with comprehensive modeling and simulation mode switching capabilities,” IEEE Trans. on Power Systems, vol. 33, no. 6, pp. 6298-6308, Nov. 2018.
- Q. Huang, V. Vittal. “Integrated transmission and distribution system power flow and dynamic simulation using mixed three-sequence/ three-phase modeling,” IEEE Trans. on Power Systems, vol. 32, no. 5, pp. 3704-3714, Sept. 2017. (2019 IEEE PES Prize Paper Award and Technical Committee Prize Paper Award)
- Q. Huang, V. Vittal. “Application of Electromagnetic Transient-Transient Stability Hybrid Simulation to FIDVR Study.” IEEE Trans. on Power Systems, vol.31, no.4, pp. 2634-2646, 2016
* For a more thorough list of publications, please visit the following website.