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
基本信息
- 批准号:2239966
- 负责人:
- 金额:$ 50万
- 依托单位:
- 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:
- 关键词:
项目摘要
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,预计超过80%的电力将通过电源转换器传输,因此越来越多的人要求将其运行寿命延长近一倍(例如,太阳能光伏系统50年,海上风电30年等),以减少总体碳足迹。另一方面,像数据中心这样的应用程序经常替换它们的电源硬件,以避免潜在的中断。大多数现有转换器安装的潜在问题是难以在不中断运行的情况下评估其健康状况或实时调整其性能。为了实现动态性能增强和可靠性改进,迫切需要以任务概况为导向的设计方法,并将数据驱动的预后健康管理与机载电源转换器无缝集成,称为“边缘智能”。拟议的“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测和预测,提高电力电子设备的健康状态和性能。如果成功,SHaPE-MaP框架将能够实时识别老化或潜在故障的模块,避免“先发制人的退役”,从而延长使用寿命。这将进一步有利于系统操作员对维护或维修的决策,以及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型转换器应用产生广泛影响,可能节省数亿美元,甚至数十亿美元。此外,一个强大的教育计划将扩大这个跨学科项目,吸引K-12和大学生。预测系统行为和健康状况一直被限制在技术设计或原型阶段,而且它们很难在最终产品中实现。这是因为需要额外的计算资源,比如笔记本电脑,来执行这些技术——这似乎是一个不切实际的场景,特别是在具有大量电源转换器的应用程序中(例如,数据中心、太阳能光伏农场等)。拟议的SHaPE-MaP框架将在实现这种边缘智能方面改变游戏规则。该项目将通过以下四个重点推进最先进的功率转换器硬件和控制系统:(i)开发集成设备和机载系统,以估计组件级别的退化;㈡现场实施健康预测技术和故障处理以及使用机器学习的转换器;(iii)使用边缘智能对转换节点进行状态估计和故障和网络攻击的弹性处理;(iv)设计与嵌入式系统协调的电路,以尽量减少计算资源。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。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 作者:Bohara, Bharat;Krishnamoorthy, Harish S.
- 通讯作者:Krishnamoorthy, Harish S.
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Harish Krishnamoorthy其他文献
Challenges and opportunities in producing high-quality edible mushrooms from lignocellulosic biomass in a small scale
- DOI:
10.1007/s00253-021-11749-2 - 发表时间:
2022-01-31 - 期刊:
- 影响因子:4.300
- 作者:
Venkatesh Balan;Weihang Zhu;Harish Krishnamoorthy;Driss Benhaddou;Jake Mowrer;Hasan Husain;Artin Eskandari - 通讯作者:
Artin Eskandari
A comparison of computational fluid dynamics predicted initial liquid penetration using rate of injection profiles generated using two different measurement techniques
使用两种不同测量技术生成的注入曲线速率对计算流体动力学预测初始液体渗透进行比较
- DOI:
10.1177/1468087417746475 - 发表时间:
2019 - 期刊:
- 影响因子:2.5
- 作者:
Haiwen Ge;Jaclyn E. Johnson;Harish Krishnamoorthy;Seong;J. Naber;Nan Robarge;E. Kurtz - 通讯作者:
E. Kurtz
Harish Krishnamoorthy的其他文献
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{{ truncateString('Harish Krishnamoorthy', 18)}}的其他基金
Collaborative Research: Development of an Autonomous Ocean Observatory Node
合作研究:自主海洋观测站节点的开发
- 批准号:
2322492 - 财政年份:2023
- 资助金额:
$ 50万 - 项目类别:
Continuing Grant
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