Rui Zhang received a B.E. degree from The University of Queensland, Brisbane, QLD, Australia, in 2009 and PhD degree from The University of Newcastle, Newcastle, NSW, Australia, in 2014, both in Electrical Engineering.
She is a lecturer at the School of Electrical Engineering and Telecommunication, UNSW, Sydney and the recipient of the 2022 Australian Research Council Discovery Early Career Researcher Award(ARC DECRA). Her research focuses on...view more
Rui Zhang received a B.E. degree from The University of Queensland, Brisbane, QLD, Australia, in 2009 and PhD degree from The University of Newcastle, Newcastle, NSW, Australia, in 2014, both in Electrical Engineering.
She is a lecturer at the School of Electrical Engineering and Telecommunication, UNSW, Sydney and the recipient of the 2022 Australian Research Council Discovery Early Career Researcher Award(ARC DECRA). Her research focuses on power system stability assessment, planning and control, considering renewable energy integration and energy storage systems through data-driven methods and AI technologies.
My Grants
ARC Discovery Early Career Research Award, " Temporal-Spatial Data Analytics for Exploring Complex Stochastic Power System Stability", 2022.
UNSW Digital Grid Future Institute Seed Funding project "Coordinated Dynamic Security Defence for Stochastic Electric Power Systems", 2022.
My Qualifications
PhD in Electrical Engineering (University of Newcastle, Australia)
My Awards
She is named among the World’s Top 2% of Scientists by Stanford in 2024
Rising Stars Women in Engineering, Asian Deans' Forum, 2022
Recipient of Australian Research Council Discovery Early Career Researcher Award (ARC DECRA 2022) in 2022.
University of Newcastle International Postgraduate Research Scholarship (UNIPRS), Sep. 2011-Apr. 2013
1st Runner-up Paper Award, 2011 IET Younger Members Exhibition & Conference, Hong Kong, Jul. 2011
My Research Supervision
Supervision keywords
Areas of supervision
Power system operation, stability and control.
Data-driven methods and AI-driven methods in power engineering.
Energy Storage System
Currently supervising
PhD Students research areas:
- Reinforcement learning for power system control
- Artificial Intelligence and data processing technology for Energy Internet
- Residential Consumer Portrait Modelling Based on Multi-dimensional Behaviour Analysis in Smart Grid
- Real time Data-driven Emergency Control for Power Systems considering Battery Energy Storage Systems
My Teaching
Teaching Experience:
ELEC9781 Special Topics - Energy Storage System(Course Convenor/Lecturer)
ELEC4612 Power System Analysis(Tutor)
More than 20 Undergraduate/ Postgraduate students mentor
https://orcid.org/0000-0003-1516-9653