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

合作研究：固态变压器的数字孪生预测可靠性建模

• 批准号: 2228872
• 负责人: JiangBiao He
• 金额: $ 23.95万
• 依托单位: University of Kentucky Research Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228872&HistoricalAwards=false
• 关键词: Collaborative; Research; Digital; Twin; Predictive

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.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

固态变压器（SST）被认为是未来电力系统的一项革命性技术。它比传统的电磁变压器结构更紧凑，在潮流控制和电能质量调节方面具有显著的可控性优势。然而，与传统变压器相比，SST的可靠性较低是制约其实用性的主要技术障碍。这是由于包括半导体晶体管、辅助电路和无源元件在内的大量器件。目前，SST的可靠性还没有得到足够的重视，这限制了SST的商业化和工业应用。该项目将为SSTs开发数据驱动的数字孪生模型，这将有助于预测部件退化和防止灾难性故障。这旨在显著提高SSTs在安全关键应用中的可靠性，例如未来的电力系统和电气化运输应用。提出的建模和设计方法将产生新型电力电子设计工具，并将实现完全集成的设计过程，从而产生新的拓扑结构，并节省大量的设计和实现时间。此外，这些方法将增强可靠性建模，即使对于新合成的体系结构，也可以从设计阶段就准确地估计可靠性。在教育影响方面，这项工作提供了将人工智能应用于电力电子工程的机会。因此，该项目的成果将提升电力电子教学课程，并为学生提供未来电力工程的有效技能。为了解决sst可靠性的挑战，本项目将开发一个全面的sst在线健康监测系统框架，以显着提高sst面对电气故障的可靠性。提出的健康监测框架将包括SSTs可能遭受的潜在电气故障的在线预测和诊断，目标是常见的半导体开关故障和高频变压器的健康退化。具体来说，将通过与电力电子构建模块集成的智能栅极驱动器监测一系列关键SST参数，因此可以在故障开始阶段预测和诊断半导体模块的退化。提出了一种新的数据驱动的数字孪生方法来预测海温转换器模块的行为，它将计算健康性能指标，使该技术比全物理模型计算更具计算效率。快速在线诊断算法将被开发并嵌入到SST微控制器中，因此可以识别和表征故障，以最大限度地减少停机成本并避免级联故障。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。