Elements: Open Access Data Generation Engine for Bulk Power System under Extreme Windstorms

要素：极端风暴下大容量电力系统的开放访问数据生成引擎

• 批准号: 2004658
• 负责人: Zhaoxia Pu
• 金额: $ 49.8万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2004658&HistoricalAwards=false
• 关键词: Elements; Open; Access; Data; Generation

The electric power grid is a critical infrastructure that is regularly disrupted by natural disasters. Currently, there is a lack of correlated data on windstorms and the physical and cyber power system infrastructure. This project addresses that gap in knowledge in dealing with high wind disaster events for preventative and restorative resiliency of electric power networks. By integrating multiple data sources into a robust simulation tool, researchers can design new methods to mitigate the impact of hurricanes and other extreme wind events on power system operations. The outcomes of the research would positively impact reliability, resiliency, and delivery of electric power to US population centers.The framework is unique and will enable inter-disciplinary research between atmospheric sciences, civil engineering, and electric power engineering. A preliminary result is provided for the Texas power system (using a digital surrogate model of the physical infrastructure and the Texas synthetic power network) under the impacts of Hurricane Harvey. The calculated damage using the model results in very similar structural damage and power systems impact as the real disaster (the ground truth model). In addition to three deliverable test cases (Texas, New York, and Florida), the project will design automated tools to create new test cases in the future for other researchers. The power system digital surrogate, correlation of datasets, and overall data generation engine will enhance the national cyberinfrastructure ecosystem. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) and the Division of Electrical, Communications and Cyber Systems (ECCS) within the NSF Directorate for Engineering.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.

电网是一个重要的基础设施，经常受到自然灾害的破坏。 目前，缺乏关于风暴以及物理和网络电力系统基础设施的相关数据。该项目解决了在处理大风灾害事件方面的知识差距，以实现电力网络的预防性和恢复性弹性。 通过将多个数据源集成到一个强大的仿真工具中，研究人员可以设计新的方法来减轻飓风和其他极端风事件对电力系统运行的影响。研究成果将对美国人口中心的电力可靠性、弹性和输送产生积极影响。该框架是独一无二的，将使大气科学、土木工程和电力工程之间的跨学科研究成为可能。初步结果提供了德克萨斯州电力系统（使用物理基础设施和德克萨斯州合成电力网络的数字代理模型）的影响下，飓风哈维。使用该模型计算的破坏结果与真实的灾难（地面实况模型）非常相似的结构破坏和电力系统影响。 除了三个可交付的测试用例（德克萨斯州、纽约和佛罗里达），该项目还将设计自动化工具，以便在未来为其他研究人员创建新的测试用例。 电力系统数字代理、数据集的相关性和整体数据生成引擎将增强国家网络基础设施生态系统。 该奖项由高级网络基础设施办公室颁发，由NSF工程理事会下属的土木、机械和制造创新部（CMMI）以及电气、通信和网络系统部（ECCS）共同支持。该奖项反映了NSF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

Effects of Roll Vortices on the Evolution of Hurricane Harvey during Landfall

滚动涡旋对飓风哈维登陆期间演变的影响

• DOI: 10.1175/jas-d-20-0270.1
• 发表时间: 2021
• 期刊: Journal of the Atmospheric Sciences
• 影响因子: 3.1
• 作者: Li, Xin;Pu, Zhaoxia;Gao, Zhiqiu
• 通讯作者: Gao, Zhiqiu

Assimilation of Radial Velocity from Coastal NEXRAD into HWRF for Improved Forecasts of Landfalling Hurricanes

• DOI: 10.1175/waf-d-20-0163.1
• 发表时间: 2021-04
• 期刊: Weather and Forecasting
• 影响因子: 2.9
• 作者: Ying Wang;Z. Pu

Targeted Hardening of Electric Distribution System for Enhanced Resilience against Earthquakes

• DOI: 10.1109/isie51582.2022.9831593
• 发表时间: 2021-10
• 期刊: 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE)
• 作者: Mahan Fakouri Fard;M. Sahraei-Ardakani;G. Ou;Mingxi Liu

Impacts of Assimilating CYGNSS Satellite Ocean-Surface Wind on Prediction of Landfalling Hurricanes with the HWRF Model

• DOI: 10.3390/rs14092118
• 发表时间: 2022-04
• 期刊: Remote. Sens.
• 作者: Z. Pu;Ying Wang;Xin Li;C. Ruf;Li Bi;A. Mehra

Accelerated Large-Scale Seismic Damage Simulation With a Bimodal Sampling Approach

• DOI: 10.3389/fbuil.2021.677560
• 发表时间: 2021-05
• 作者: M. Sheibani;Ge Ou