Postdoctoral Research Fellow, Dr Feifei Bai
Postdoctoral Research Fellow, Dr Feifei Bai

Postdoctoral Research Fellow
f.bai@uq.edu.au
+61 7 3346 1006

Feifei (Ivy) Bai received her Bachelor degree in Electrical Engineering and its Automation from Southwest Jiaotong University, Chengdu, China in 2010 and she received her PhD Degree in Power System and its Automation from Southwest Jiaotong University in 2016.

During her PhD, she did research in the University of Tennessee at Knoxville, USA from Sep. 2012 to Dec. 2014.

Before joing the Global Change Institute Clean Energy group, Dr Bai was a postdoctoral research fellow with the UQ School of Information Technology and Electrical Engineering. Her research interests include PV integration impacts, battery energy storage integration research, power system low-frequency oscillation analysis and control.

Publications

  • Journal Article: Measurement-based power system frequency dynamic response estimation using geometric template matching and recurrent artificial neural network
  • Bai, Feifei, Wang, Xiaoru, Liu, Yilu, Liu, Xinyu, Xiang, Yue and Liu, Yong (2016) Measurement-based power system frequency dynamic response estimation using geometric template matching and recurrent artificial neural network. CSEE Journal of Power and Energy Systems, 2 3: 10-18. doi:10.17775/CSEEJPES.2016.00030
  • Journal Article: A measurement-based approach for power system instability early warning
  • Bai, Feifei, Liu, Yong, Liu, Yilu, Sun, Kai, Bhatt, Navin, Del Rosso, Alberto, Farantatos, Evangelos and Wang, Xiaoru (2016) A measurement-based approach for power system instability early warning. Protection and Control of Modern Power Systems, 1 1-9. doi:10.1186/s41601-016-0014-0
  • Journal Article: Investigation on impacts of alternative generation siting in power grids from the view of complex network theory
  • Xiang, Yue, Liu, Yilu, Liu, Junyong, Bai, Feifei, Liu, Yong and Huang, Cheng (2016) Investigation on impacts of alternative generation siting in power grids from the view of complex network theory. Electric Power Components and Systems, 44 7: 820-831. doi:10.1080/15325008.2015.1137995

 

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