Rising Stars 2020:

Zhen Dai

Postdoctoral Researcher

University of Toronto

PhD '19 University of Toronto

Areas of Interest

  • Energy
  • Electric Power Systems


Generator Outage Identification Using Synchrophasor Measurements


Identification of outages in power systems is crucial to raise situational awareness and prevent cascading failures. As a result of the wide adoption of phasor measurement units (PMUs), synchronized measurements with high temporal resolution provide new opportunities for event detection and identification in electric power systems. The loss of generation will rapidly change system operating conditions and may threaten system stability. In this research, two novel algorithms for generator outage localization are proposed utilizing (1) line flow measurements and linear sensitivity factors, and (2) voltage measurements, system admittance matrix, generator internal reactances, and generator injections prior to the outage events. A clustering technique based on QR decomposition is also proposed to group generators together based on their impacts. The outage can then be localized to the originating cluster with zero misidentification with limited measurements. All the proposed identification algorithms can be implemented for online application in utility control centers in a complementary fashion to achieve the best overall results.


Zhen Dai received the Ph.D. degree in electrical engineering at University of Toronto, Canada, where she continued her research as a postdoctoral fellow. Her research interests are focused on event detection and identification in electric power systems with the help of synchronized measurements. She is passionate about delivering engineering solutions, particularly wide-area applications, to raise situational awareness in power systems. She also holds a Master of Applied Science degree from UofT and a Bachelor of Engineering degree from Tsinghua University, China.