IEEE Power Electronics Magazine - March 2021 - 27

robust condition monitoring solutions for PES. The
-practical applications and field operations are still limited,
which are yet to be demonstrated.

IEEE Trans. Power Electron, vol. 25, no. 11, pp. 2734-2752, Nov. 2010. doi:

About the Authors

pp. 2262-2273, Nov. 2017. doi: 10.1109/JPROC.2017.2756596.

Shuai Zhao (szh@et.aau.dk) received the BE (Hons),
ME, and Ph.D. degrees in information and communication engineering from Northwestern Polytechnical University, Xi'an, China, in 2011, 2014, and 2018, respectively.
He is currently a postdoctoral researcher with the Center
of Reliable Power Electronics (CORPE), Department of
Energy Technology, Aalborg University, 9220 Aalborg,
Denmark. From September 2014 to September 2016, he
was a visiting Ph.D. student with the Department of
Mechanical and Industrial Engineering at the University
of Toronto, Toronto, ON, Canada, with a scholarship from
China Scholarship Council (CSC). In August 2018, he was
a visiting scholar with the Power Electronics and Drives
Laboratory, Department of Electrical and Computer Science at the University of Texas at Dallas, Richardson, TX,
United States. His research interests include system informatics, intelligent condition monitoring, diagnostics &
prognostics, and tailored AI tools for power electronic systems.
Huai Wang (hwa@et.aau.dk) received BE degree in
electrical engineering from Huazhong University of Science and Technology, Wuhan, China, in 2007 and Ph.D.
degree in power electronics, from the City University of
Hong Kong, Hong Kong, in 2012. He is currently Professor with the Center of Reliable Power Electronics
(CORPE), Department of Energy Technology at Aalborg
University, 9220 Aalborg, Denmark. He was a visiting scientist with the ETH Zurich, Switzerland, from August to
September 2014, and with the Massachusetts Institute of
Technology, United States, from September to November
2013. He was with the ABB Corporate Research Center,
Switzerland, in 2009. His research addresses the fundamental challenges in modeling and validating power electronic component failure mechanisms and application
issues in system-level predictability, condition monitoring, circuit architecture, and robustness design. He leads
a project on Light-AI for Cognitive Power Electronics. Dr.
Wang received the Richard M. Bass Outstanding Young
Power Electronics Engineer Award from the IEEE Power
Electronics Society in 2016, and the Green Talents Award
from the German Federal Ministry of Education and
Research in 2014. He is currently the Chair of the IEEE
PELS/IAS/IES Chapter in Denmark. He serves as an associate editor of IEEE Journal of Emerging and Selected
Topics in Power Electronics and IEEE Transactions on
Power Electronics.

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TPEL.2020.3023469.
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for batteries with machine learning, " Nature, vol. 578, no. 7795, pp. 397-402,
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References

deep learning at the edge for scalable reliability modeling of Si-MOSFET

[1] S. Yang, D. Xiang, A. Bryant, P. Mawby, L. Ran, and P. Tavner, " Condition

power electronics converters, " IEEE Internet Things J., vol. 6, no. 5, pp.

monitoring for device reliability in power electronic converters: A review, "

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IEEE Power Electronics Magazine - March 2021

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