Grants and Contracts Details
Description
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 controllability advantage
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, due to
the large device count including semiconductor transistors, auxiliary circuits, passive components and
internal connections. Currently, the reliability of SST has received little attention.
To address the problem, our team at the University of Kentucky and Dr. Mohammad Agamy’s team at State
University of New York at Albany will collaborate and develop a comprehensive systematic framework of
online health monitoring for SSTs to significantly improve the reliability to electric faults. The proposed
health monitoring framework will include online prognosis and diagnosis of potential electrical faults that
could occur to SST, targeting common semiconductor switching faults and health degradation in the 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 (PEBBs), so degradation
in the semiconductor modules can be predicted and diagnosed during the fault incipient stage. A novel data
driven digital twin approach is proposed to predict the behavior of the SST converter modules and it will
compute specific health performance indices to make it more computationally effective 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 the downtime cost and avoid
cascaded failures.
Status | Active |
---|---|
Effective start/end date | 8/1/23 → 7/31/26 |
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.