Collaborative Research: Digital Twin Predictive Reliability Modeling of Solid-State Transformers

  • He, Jiangbiao (PI)

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.
StatusActive
Effective start/end date8/1/237/31/26

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