CAREER: Enhancing the State of Health and Performance of Electronics via in-situ Monitoring and Prediction (SHaPE-MaP) - Toward Edge Intelligence in Power Conversion

职业：通过原位监控和预测 (SHAPE-MaP) 提高电子设备的健康状况和性能 - 迈向功率转换领域的边缘智能

• 批准号: 2239966
• 负责人: Harish Krishnamoorthy
• 金额: $ 50万
• 依托单位: University of Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-15 至 2028-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239966&HistoricalAwards=false
• 关键词: CAREER; Enhancing; State; Health; Performance

With over 80 % of electricity expected to flow through power converters by 2030, there is a growing requirement to nearly double their operational lifetime (e.g., 50 years for solar PV systems, 30 years for offshore wind, etc.) for reducing the overall carbon footprint. On the other hand, applications like data centers frequently replace their power hardware preemptively to avoid potential outages. The underlying problem with most existing converter installations is the difficulty to assess their health or adapt their performance in real-time without disrupting their operation. To achieve dynamic performance enhancement and reliability improvement, there is a critical need for mission profile-oriented design methods and seamless integration of data-driven prognostic health management with power converters onboard, referred to as ‘Edge Intelligence’. The proposed ‘SHaPE-MaP’ framework aims to enhance the State of Health and performance of Power Electronics via in-situ Monitoring and Prediction using onboard FPGAs or processors for edge intelligence. If successful, the SHaPE-MaP framework will enable the identification of aged or potentially failing modules in real-time and avoid ‘preemptive decommissioning’, thereby increasing the operational life. It will further benefit the system operators in decision-making about maintenance or repair, and the supply chain team to better estimate the logistics or manage the inventory items. Hence, this project will have a widespread impact on most large-scale converter applications, potentially saving hundreds of millions, if not billions of dollars. Moreover, a robust education program will augment this interdisciplinary project to engage K-12 and college students.Predicting system behavior and health has been restricted to the technology design or prototyping phases, and they are hardly implemented in the final products. It is due to the need for additional computing resources, such as a laptop, to execute these techniques – a seemingly impractical scenario, especially in applications with a large number of power converters (e.g., data centers, solar PV farms, etc.). The proposed SHaPE-MaP framework will be a game-changer in enabling such edge intelligence. This project will advance the state-of-the-art power converter hardware and control systems via the following four key thrusts: (i) Development of integrated devices and onboard systems to estimate degradation at the component level; (ii) In-situ implementation of health prediction techniques and fault handling along with the converters using machine learning; (iii) Status estimation and resilient handling of faults and cyber-attacks at converter nodes using edge intelligence; and (iv) Design of coordinated circuits with embedded systems to minimize the computing resources.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.

到2030年，预计有80％以上的电力将通过电力转换器流动，因此越来越多的要求将其运营寿命翻一番（例如，太阳能光伏系统50年，海上风能30年），以减少整体碳足迹。另一方面，诸如数据中心之类的应用程序经常抢先替换其功率硬件，以避免潜在的停电。大多数现有转换器安装的根本问题是难以评估其健康或实时调整其性能而不会破坏其操作。为了实现动态性能的提高和可靠性提高，至关重要的是，以任务配置文件为导向的设计方法以及将数据驱动的预后健康管理与功率转换器无缝集成在船上，称为“边缘智能”。拟议的“形状图”框架旨在通过使用机上FPGA或处理器进行边缘智能来增强电力电子设备的健康状况和电力电子的性能。如果成功的话，形状映射框架将实时识别老化或潜在失败的模块，并避免“先发制人的退役”，从而提高运营寿命。它将进一步使系统操作员在有关维护或维修的决策中，供应链团队更好地估计物流或管理库存项目。因此，该项目将对大多数大规模转换器应用程序产生宽度影响，并有可能节省数亿美元（即使不是数十亿美元）。此外，强大的教育计划将扩大该跨学科的项目，以吸引K-12和大学生。预测的系统行为和健康仅限于技术设计或原型阶段，并且在最终产品中几乎没有实施。这是由于需要其他计算资源（例如笔记本电脑）执行这些技术 - 尤其是不切实际的情况，尤其是在具有大量电源转换器的应用中（例如，数据中心，太阳能PV Farm等）。所提出的形状映射框架将是实现这种边缘智能的游戏改变者。该项目将通过以下四个关键推力来推动最先进的电源转换器硬件和控制系统：（i）开发集成设备和板载系统以估计组件级别的退化； （ii）使用机器学习以及转换器的健康预测技术和故障处理的原位实施； （iii）使用Edge Intelligence在转换器节点处的故障和网络攻击的状态估计和抗性处理； （iv）与嵌入式系统的协调电路设计以最大程度地减少计算资源。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估来获得的支持。

项目成果

Prognostic Health Monitoring of DC Microgrid with Fault Detection and Localization using Machine Learning Techniques

使用机器学习技术进行故障检测和定位的直流微电网的预测健康状况监控

• DOI: 10.1109/ecce53617.2023.10362739
• 发表时间: 2023
• 期刊: IEEE
• 作者: Bohara, Bharat;Krishnamoorthy, Harish S.
• 通讯作者: Krishnamoorthy, Harish S.