SBIR Phase I: No-code electric grid analytics platform for predictive maintenance planning and emergency response

SBIR 第一阶段：用于预测性维护规划和应急响应的无代码电网分析平台

• 批准号: 2136505
• 负责人: Sayonsom Chanda
• 金额: $ 25.6万
• 依托单位: SYNC, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-15 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2136505&HistoricalAwards=false
• 关键词: SBIR; Phase; No; code; electric

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is in helping in the design of a cost-effective resiliency strategy for defense against the impacts of climate change. Today, the U.S. experiences significantly more weather events imposing substantially more financial burden compared to 40 years ago. As climate change accelerates, communities will be forced to endure increased financial burdens protecting against and mitigating its impacts. Research carried out in this SBIR project is intended to help reduce these expenses. Utilities, cities, insurance companies, and municipalities are stakeholders in these resiliency efforts and will be looking for new tools to help mitigate the effects and reduce the costs of climate change. The research in this project will enable utilities to reduce resilience-related costs and reduce impact on businesses and local economies due to power outages. Such actions may benefit all communities, particularly poorer and marginal communities that often endure the worst impacts of climate change.This Small Business Innovation Research Phase I project aims to develop predictive analytics for tropical storms and wildfires and to integrate this functionality into a power grid analytics software platform. Three artificial intelligence/machine learning (AI/ML) tools will be implemented, qualitatively expanding on early prototypes: (1) in the satellite imagery (SI) domain, an optimized combination of deep-learning neural network (DLNN) techniques will be trained on large-scale satellite images, resulting in the world's first tree growth tracking and species identification tool; (2) a "Virtual Wind Tunnel" (VWT) will be augmented with computational fluid dynamics (CFD) and empirical physics modeling to estimate the probability of trees damaging power transmission assets during weather events forecast; and (3) towards a no-code user interface, existing natural language processing (NLP) will be expanded, with the goal of processing queries from engineers unfamiliar with AI/ML. Key questions addressed by the research include whether the software platform will be able to adapt to new utility customers and service areas without sacrificing performance, whether increased data resolution can be effectively leveraged to better predictive power, and whether the platform can continuously improve event prediction over time by learning from historical grid data.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.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力在于帮助设计具有成本效益的弹性策略，以防御气候变化的影响。如今，与 40 年前相比，美国经历的天气事件明显增多，造成了更大的经济负担。随着气候变化的加速，社区将被迫承受日益增加的财务负担，以防范和减轻其影响。 SBIR 项目中进行的研究旨在帮助减少这些费用。公用事业公司、城市、保险公司和市政当局是这些抗灾努力的利益相关者，并将寻找新的工具来帮助减轻气候变化的影响并降低成本。该项目的研究将使公用事业公司能够降低与弹性相关的成本，并减少停电对企业和当地经济的影响。 此类行动可能会使所有社区受益，特别是经常遭受气候变化最严重影响的贫困和边缘社区。这个小型企业创新研究第一阶段项目旨在开发热带风暴和野火的预测分析，并将此功能集成到电网分析中软件平台。 将实施三种人工智能/机器学习（AI/ML）工具，对早期原型进行定性扩展：（1）在卫星图像（SI）领域，将训练深度学习神经网络（DLNN）技术的优化组合基于大规模卫星图像，产生了世界上第一个树木生长跟踪和物种识别工具； (2) “虚拟风洞”(VWT) 将通过计算流体动力学 (CFD) 和经验物理模型进行增强，以估计在天气事件预报期间树木损坏输电资产的概率； (3) 现有的自然语言处理 (NLP) 将扩展到无代码用户界面，目标是处理不熟悉 AI/ML 的工程师的查询。研究解决的关键问题包括软件平台是否能够在不牺牲性能的情况下适应新的公用事业客户和服务领域，是否可以有效地利用增加的数据分辨率来提高预测能力，以及平台是否可以持续改进事件预测。该奖项反映了 NSF 的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。