Category: Ten

Chee-Wooi Ten’s Research Is Subject of Advisor News Article

Associate Professor Chee-Wooi Ten, Electrical and Computer Engineering, was cited in the article, “Reports Summarize Engineering Study Results from Electrical & Computer Engineering Department (Premium Calculation for Insurance Businesses Based On Cyber Risks In IP-based Power Substations),” published August 11, 2020 in Advisor News.

Ten is a member of the Institute of Computing and Cybersystems (ICC) at Michigan Tech and the ICC’s Center for Cyber-Physical Systems.

The paper emphasizes a framework of premium calculation for cyber insurance businesses by modeling potential electronic intrusion with steady-state simulation results and its direct hypothesized impacts, according to the article, citing a NewsRx press release.

The article discussed Ten’s National Science Foundation (NSF) Cyber-Physical Systems grant, “CPS: Medium: Collaborative Research: An Actuarial Framework of Cyber Risk Management for Power Grids.” Assistant Professor Yeonwoo Rho, Mathematical Sciences, is co-PI on the award. The three-year $349K project was awarded in August 2017. Read the abstract and view additional CPS and ICC research projects here, . View the award at NSF.com.

The Institute of Computing and Cybersystems, founded in 2015, promotes collaborative, cross-disciplinary research and learning experiences in the areas of computing education, cyber-physical systems, cybersecurity, data sciences, human-centered computing, and scalable architectures and systems for the benefit of Michigan Technological University and society at large.

It works to provide faculty and students the opportunity to work across organizational boundaries to create an environment that mirrors contemporary technological innovation.

Advisor News is published by InsuranceNewsNet, which describes itself as on the forefront of communicating breaking news and original insights to the industry. With thousands of news sources and hundreds of original articles, the site provides premium content typically only available through proprietary news outlets.


Dr. Kun Zhu of MISO to Present Lecture on U.S. Power Grid, March 2

The Institute of Computing and Cybersystems and the Department of Electrical and Computer Engineering will present a lecture by Dr. Kun Zhu on Monday, March 2, 2020, at 3:00 p.m., in EERC 501. The title of Dr. Zhu’s talk is “Power Grid Operations – Beyond Physics.

Dr. Zhu holds a Ph.D. in electrical engineering from Iowa State University. He has 20 years’ experience in the power industry, including 17 years at MISO, an independent, not-for-profit organization that delivers safe, cost-effective electric power across 15 U.S. states and the Canadian province of Manitoba.

Dr. Zhu’s presentation will provide a high level introduction to how regional operators manage the power grid in the U.S. He will discuss how energy markets and balancing authorities (those responsible for maintaining the electricity balance within their respective regions) manage their regions and interact with each other; differences in how energy and transmission assets are managed; and the function of Regional Transmission Organizations (RTO).

At MISO, Dr. Zhu’s experience expands across planning, operations, and tariff administration. Currently, he is the manager of generator interconnection and chair of the SPIDER Working Group (SPIDER), a working unit of North America Electric Reliability Cooperation (NERC).  

MISO operates one of the world’s largest energy markets with more than $29 billion in annual gross market energy transactions. 


Chee-Wooi Ten is PI of R and D Agreement with University of California Riverside

Chee-Wooi Ten

Chee-Wooi Ten (ECE), a member of Michigan Tech’s Center for Agile and Interconnected Microgrids and the ICC’s Center for Cyber-Physical Systems, is the principal investigator on a 17-month project that has received a $99,732 research and development cooperative agreement with the University of California Riverside. The project is entitled, “Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks.”