Buxin She | Electrical Engineering | Young Scientist Award

doctorate at University of Tennessee Knoxville

👨‍🏫 Buxin She, an accomplished Electrical Engineer at Pacific Northwest National Laboratory, is a distinguished figure in the field of Power Systems. Her academic journey commenced at the University of Tennessee Knoxville, where she earned her Ph.D. in Power System from the Department of Electrical Engineering and Computer Science between Jan 2020 and Dec 2023. Prior to this, she pursued her Master’s and Bachelor’s degrees in Power System at Tianjin University in China. 📚

🌐 Professional Profiles:

📚 Education

Ph.D. Power System – University of Tennessee Knoxville, Tennessee, USA, Jan 2020 – Dec 2023 Master Power System – Tianjin University, Tianjin, China, Sep 2017 – Dec 2019 Bachelor Power System – Tianjin University, Tianjin, China, Sep 2013 – Jun 2017

💡 Skills Summary

Power Knowledge: Microgrid control, Inverter-based resources (IBR) modeling and control, Security-constrained economic dispatch, Distribution system plan. Techniques Knowledge: Control theory, Convex optimization, Deep learning, (Deep) Reinforcement learning. Power Simulator: Matlab/Simulink, ATP-EMTP, Andes, Pandapower/Matpower. AI/Control/Optim. Toolbox: Pytorch, Tensorflow, Sympy, Scipy, Gurobipy, CVX.

🔬 Research Summary

Microgrid & IBR Control Developed Inverter PQ Control with Trajectory Tracking Capability using physics-informed reinforcement learning. Proposed Decentralized and Cooperated V-f Control for islanded microgrids. Explored the fusion of Model-free Reinforcement Learning (MFRL) with Microgrid Control. Created a Microgrid Controller Design ensuring Certified Stability and Domain of Attraction. Introduced Time Delay Compensation for HVDC Damping Control. Security-constrained economic dispatch Proposed Virtual Inertia Scheduling (VIS) for IBR-penetrated power systems. Extended the concept of VIS to ensure Microgrid stability under large disturbances. Distribution System Operation and Plan Conducted research on active distribution networks, operational boundaries, and capability curves. Thermal Energy Storage Participated in research for efficient energy management using deep learning techniques for heat flux prediction.

🏆 Review Service and Awards

Journal Editor: Guest Student Editor for IET Renewable Power Generation. Journal Reviewer: Serves as a reviewer for various IEEE and other reputable journals. Conference Reviewer: Contributed as a reviewer for IEEE conferences. Awards: Recognized as an excellent reviewer for Journal of Modern Power Systems and Clean Energy (MPCE) and Open Access Journal of Power and Energy (OAJPE).

Top Noted Publications 📚:

Boundary supply capability for distribution systems: concept, indices and calculation Paper Published in 2018 Cited by 23

Fusion of Microgrid Control with Model-free Reinforcement Learning: Review and Vision Paper Published in 2022 Cited by 18

Decentralized and Coordinated Vf Control for Islanded Microgrids Considering DER Inadequacy and Demand Control Paper Published in 2023 Cited by 9

Mathematical model and mechanism of TSC curve for distribution networks Paper Published in 2022 Cited by 8

Total supply and accommodation capability curves for active distribution networks: Concept and model Paper Published in 2021 Cited by 8

Gas transmission capability curve of natural gas system: Concept and steady-state model Paper Published in 2021 Cited by 8

Locating and sizing of partition flexible interconnection converter station in large urban power grids Paper Published in 2019 Cited by 7

A distributed consensus-based optimal energy management approach in DC microgrids Paper Published in 2022 Cited by 6

Machine learning–assisted prediction of heat fluxes through thermally anisotropic building envelopes Paper Published in 2023 Cited by 5

Geometric property of distribution system security region: Size and shape Paper Published in 2022 Cited by 5

Total supply capability and distribution system security region considering certain components N-1 Paper Published in 2019 Cited by 5

Virtual Inertia Scheduling (VIS) for Real-time Economic Dispatch of IBRs-penetrated Power Systems Paper Published in 2023 Cited by 4

Yuchao Fan| Mechanical Engineering | Young Scientist Award

👨‍🏫Yuchao Fan, stands as a distinguished Student/Member in the domain of Neuropharmacoogy. Holding a Ph.D. candidate of Measuring Technology and Instruments, School of Mechanical Engineering, Dalian University of Technology of China, their professional journey exemplifies dedication and expertise. 📚

🎓 Education and Work

Ph.D. Candidate (Measuring Technology and Instruments) School of Mechanical Engineering, Dalian University of Technology, China 09/2018 – Present Software Engineering (Robotics) Googol Technology Co., Ltd, China 06/2015 – 07/2018 M.S. in Control Engineering School of Mechanical and Electrical Engineering, Heilongjiang University, China 09/2013 – 06/2015 B.S. in Electrical Engineering and Automation School of Mechanical and Electrical Engineering, Heilongjiang University, China 09/2009 – 06/2013

🔬📚 Research Experience

National Key Research and Development Program of China Developed a multi-axis online measurement system for machine tool monitoring. Created real-time error compensators and various error compensation models to enhance processing accuracy. Department of Science and Technology of Liaoning Province Developed a five-degree-of-freedom offline measuring instrument for machine tool geometric error measurement. Created software for spatial error compensation applicable to various machine tool configurations.

🏅📜 Scientific Research Achievements

Self-compensation method for dual-beam roll angle measurement of linear stages” (Optics Express, 2021) Fast volumetric error assessment of a gantry-type machine using multi-degree-of-freedom laser-based sensors (International Journal of Advanced Manufacturing Technology, 2022) An Innovative Dual-Axis Precision Level Based on Light Transmission and Refraction for Angle Measurement (Applied Sciences, 2020) Modeling and compensation of enhanced volumetric error of machine tools containing crosstalk errors” (Precision Engineering, 2023 – Accepted)