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.

预计到 2030 年，超过 80% 的电力将流经电力转换器，因此越来越需要将其使用寿命延长近一倍（例如，太阳能光伏系统 50 年，海上风电 30 年等），以减少总体碳排放另一方面，像数据中心这样的应用程序经常提前更换其电源硬件，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在没有情况下实时评估其运行状况或调整其性能。为了实现动态性能增强和可靠性改进，迫切需要面向任务配置文件的设计方法以及数据驱动的预测健康管理与机载电源转换器的无缝集成，称为“边缘智能”。 “SHAPE-MaP”框架旨在通过使用板载 FPGA 或边缘智能处理器进行现场监控和预测来提高电力电子设备的健康状况和性能。如果成功，SHAPE-MaP 框架将能够识别老化或老化的设备。实时发现潜在故障的模块并避免“抢先退役”，从而延长使用寿命，这将进一步有利于系统运营商进行维护或维修决策，以及供应链团队更好地估计物流或管理库存。因此，该项目将对大多数大型转换器应用产生广泛影响，可能节省数亿甚至数十亿美元。此外，强大的教育计划将增强这一跨学科项目，以吸引 K-12 和大学生。 .预测系统行为和健康状况这些技术仅限于技术设计或原型设计阶段，并且很难在最终产品中实现，这是因为需要额外的计算资源（例如笔记本电脑）来执行这些技术——这似乎是不切实际的场景，尤其是在具有以下功能的应用中。大量电力转换器（例如数据中心、太阳能光伏发电场等）。拟议的 SHaPE-MaP 框架将成为实现此类边缘智能的游戏规则改变者。该项目将推动最先进的技术发展。电源转换器硬件和控制系统，通过以下四个关键重点：（i）开发集成设备和机载系统，以估计组件级别的退化；（ii）使用机器学习与转换器一起现场实施健康预测技术和故障处理； (iii) 使用边缘智能对转换器节点的故障和网络攻击进行状态估计和弹性处理；以及 (iv) 设计具有嵌入式系统的协调电路，以最大限度地减少计算资源。该奖项的法定使命，并被认为值得通过以下方式获得支持：评估使用基金会的智力价值和更广泛的影响审查标准。

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.