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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力在于帮助设计一种具有成本效益的弹性战略，以防御气候变化的影响。如今，与40年前相比，美国的天气事件大大增加了财务负担。随着气候变化的加速，社区将被迫忍受增加金融负担，以防止和减轻其影响。在这个SBIR项目中进行的研究旨在帮助减少这些费用。公用事业，城市，保险公司和市政当局是这些弹性工作中的利益相关者，并将寻找新工具来帮助减轻效果并降低气候变化的成本。该项目的研究将使公用事业能够降低与弹性相关的成本，并减少由于停电而对企业和当地经济的影响。 此类行动可能使所有社区，尤其是较贫穷和边际社区受益，这些社区通常会忍受气候变化的最严重影响。本小型企业创新研究阶段I项目旨在开发热带风暴和野火的预测分析，并将此功能整合到电网分析软件平台中。 将实施三种人工智能/机器学习（AI/ML）工具，对早期原型进行质量扩展：（1）在卫星图像（SI）域中，深入学习神经网络（DLNN）技术的优化组合将在大型卫星图像中训练，从而在世界上的第一个树木成长工具中进行训练，从而在世界上进行了培训。 （2）“虚拟风隧道”（VWT）将通过计算流体动力学（CFD）和经验物理建模来增强，以估计天气事件预测期间树木破坏电力传输资产的可能性； （3）朝着无代码用户界面，将扩展现有的自然语言处理（NLP），目的是处理不熟悉AI/ML的工程师的查询。 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.