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He Receives NSF Funding to Develop Digital Twin Predictive Reliability Models for Solid State Transformers

September 27, 2023

JiangBiao He, associate professor and endowed L. Stanley Pigman Faculty Fellow in the Department of Electrical and Computer Engineering, serves as PI on the project, which will receive $480,000 over three years. 

JiangBiao He, associate professor and endowed L. Stanley Pigman Faculty Fellow in the Department of Electrical and Computer Engineering, has received  new funding from the National Science Foundation (NSF) for his project,‚ÄúCollaborative Research: Digital Twin Predictive Reliability Modeling of Solid-State Transformers." The collaborative research project with State University of New York at Albany, is funded in the total amount of $480,000 over three years. On the project, UK serves as the lead organization and He serves as the principal investigator (PI). 

He and his research partners hope to develop a system that improves the reliability of solid-state transformers (SST) so that more industries can adopt SST systems to replace traditional power transformers. 

"Power transformers are used in almost every power grid transmission and distribution system as well as high power conversion systems (electric aircraft, ships, fast EV chargers, etc.)," He said. "However, their bulky footprint, low energy efficiency, and lack of power controls and grid services are the major drawbacks limiting the performance of the present electric power systems."

He believes that SST is an emerging and revolutionary technology that can well replace conventional power transformers. SSTs dramatically improves the power density and efficiency of power transformers, in addition to providing flexible power flow control and power quality regulation. However, low reliability is a major bottleneck preventing the further development and commercialization of SSTs.

In He's project, a portfolio of innovative digital twin predictive reliability models will be developed, which is expected to significantly enhance the reliability of SSTs.

He joined the Department of Electrical and Computer Engineering in 2019, after multiple years of industry R&D experience at GE Global Research and Rockwell Automation. His research interests include power electronic converters, motor drives, transportation electrifications and renewable energy. He has served as a PI on numerous projects sponsored by NSF, DOE, NASA, DOD, high-tech industries and non-profit organizations. More information about his research can be found on the AMPERE lab's website

 

ABSTRACT

Solid state transformer (SST) is deemed as a revolutionary technology for future power systems. It is much more compact than the conventional electromagnetic transformer, with significant advantages of controllability both in power flow control and power quality regulation. However, one major technical barrier that constrains the practicality of SST is the low reliability compared to the conventional transformers. This is due to the large device count including semiconductor transistors, auxiliary circuits, and passive components. Currently, the reliability of SST has received little attention, which constrains their commercialization and adoption by industry. This project will develop data-driven digital twin models for SSTs that will facilitate prediction of component degradation and prevention of catastrophic failures. This is aimed to significantly improve the reliability of SSTs for safety-critical applications, such as future power systems and electrified transportation applications. The proposed modeling and design methods will result in new classes of power electronics design tools and will enable a fully integrated design process that will generate new topologies and save substantial design and implementation time. Further, these approaches will enhance reliability modeling where reliability can be accurately estimated from at design stage even for newly synthesized architectures. Regarding educational impact, this work presents an opportunity to apply artificial intelligence to power electronics engineering. Hence, the outcome of the project will upgrade power electronics teaching curricula and provide students with an effective skillset for future power engineering.

To address the challenge of reliability of SSTs, this project will develop a comprehensive systematic framework of online health monitoring for SSTs to significantly improve the reliability in the face of electric faults. The proposed health monitoring framework will include online prognosis and diagnosis of potential electrical faults that SSTs could be subject to, targeting common semiconductor switching faults and health degradation in high-frequency transformers. Specifically, a portfolio of critical SST parameters will be monitored through a smart gate driver that will be integrated with the power electronic building blocks, so degradation in the semiconductor modules can be predicted and diagnosed during the fault inception stage. A novel data-driven digital twin approach is proposed to predict the behavior of the SST converter modules and it will compute health performance indices to make the technique more computationally efficient compared to full physical model computations. Fast online diagnostic algorithm will be developed and embedded in the SST microcontroller, so a fault can be identified and characterized, to minimize downtime cost and avoid cascading failures.