ASCENT: Sensor-enabled Wildfire Awareness and Risk Management (WARM) for Electric Power Infrastructure
ASCENT:电力基础设施中基于传感器的野火意识和风险管理 (WARM)
基本信息
- 批准号:2132904
- 负责人:
- 金额:$ 150万
- 依托单位:
- 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:2021-09-15 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The electric grid is susceptible to increasing wildfire risks, particularly in forested and rural areas. This is primarily due to a lack of risk awareness in such remote locations, which makes prevention and response management difficult. This NSF project addresses this problem by designing and testing a Wildfire Awareness and Risk Management (WARM) system that will use Internet of Things (IoT) wireless sensors to monitor the environment around remote electric power infrastructures and support resilient grid operation during high wildfire risk periods. The project will bring transformative changes to the way power utilities, regulatory agencies, and municipal managers react to wildfire threats by raising their real-time situational awareness and developing methods and strategies that increase their options for successful prevention and rapid response. These changes will be achieved by making advances in remote sensing, wireless communication, power system security analysis, and optimization. The intellectual merit of this project lies in understanding, anticipating, measuring, and mitigating wildfire risks associated with the electric grid. The sensor suites and risk management approaches developed through this project can have broader impacts in disaster mitigation, environmental monitoring, and public safety.Despite the critical societal impacts of wildfires and power outages, there are fundamental gaps in sensing and decision-making methods for wildfire risk management related to the electric grid. We address this knowledge gap by: (1) proposing a self-sustainable, low-maintenance sensor suite that will accurately and quickly detect wildfire risks occurring in its vicinity, and (2) creating data-informed wildfire risk management frameworks for preemptive and responsive decision-making that are consistent with evolving regulatory constraints and policy objectives. The result is a comprehensive WARM system that combines currently available data on power systems and ambient conditions (such as weather) with data from the sensor suites to provide fundamental insights and innovative approaches for improved spatio-temporal management of wildfire risks. The expected benefits of this project include significant improvements in the operational reliability of the power system during periods of elevated wildfire risk and more informed decisions about power shutoffs, leading to fewer, shorter, and more targeted power outages.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.
电网易受野火风险增加的影响,特别是在森林和农村地区。这主要是由于这些偏远地区缺乏风险意识,使预防和应对管理变得困难。该NSF项目通过设计和测试野火意识和风险管理(WARM)系统来解决这个问题,该系统将使用物联网(IoT)无线传感器来监控远程电力基础设施周围的环境,并在高野火风险期间支持弹性电网运行。该项目将为电力公司、监管机构和市政管理人员应对野火威胁的方式带来变革性变化,提高他们的实时态势感知,并制定方法和战略,增加他们成功预防和快速响应的选择。这些变化将通过在遥感、无线通信、电力系统安全分析和优化方面的进步来实现。该项目的智力价值在于理解,预测,测量和减轻与电网相关的野火风险。通过该项目开发的传感器套件和风险管理方法可以在减灾、环境监测和公共安全方面产生更广泛的影响。尽管野火和停电会产生严重的社会影响,但与电网相关的野火风险管理的传感和决策方法仍存在根本性的差距。我们通过以下方式解决这一知识差距:(1)提出一种自我可持续、低维护的传感器套件,可以准确、快速地检测附近发生的野火风险;(2)创建数据知情的野火风险管理框架,以便根据不断变化的监管约束和政策目标做出先发制人的响应性决策。其结果是一个全面的WARM系统,该系统将电力系统和环境条件(如天气)的当前可用数据与传感器套件的数据相结合,为改进野火风险的时空管理提供基本见解和创新方法。该项目的预期效益包括在野火风险升高期间显著提高电力系统的运行可靠性,以及做出更明智的停电决策,从而减少停电次数,缩短停电时间,提高停电针对性。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
期刊论文数量(4)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
ECO: Enabling Energy-Neutral IoT Devices Through Runtime Allocation of Harvested Energy
ECO:通过运行时分配所收集的能量来实现能源中性物联网设备
- DOI:10.1109/jiot.2021.3106283
- 发表时间:2022
- 期刊:
- 影响因子:10.6
- 作者:Tuncel, Yigit;Bhat, Ganapati;Park, Jaehyun;Ogras, Umit Y.
- 通讯作者:Ogras, Umit Y.
Co-Optimization of Power Line Shutoff and Restoration Under High Wildfire Ignition Risk
高野火点火风险下电力线路关闭与恢复的协同优化
- DOI:10.1109/powertech55446.2023.10202865
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Rhodes, Noah;Roald, Line A.
- 通讯作者:Roald, Line A.
A Domain-Specific System-On-Chip Design for Energy Efficient Wearable Edge AI Applications
适用于节能可穿戴边缘人工智能应用的特定领域片上系统设计
- DOI:10.1145/3531437.3539711
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Tuncel, Yigit;Krishnakumar, Anish;Chithra, Aishwarya Lekshmi;Kim, Younghyun;Ogras, Umit
- 通讯作者:Ogras, Umit
Mitigation of Saturated Cut-sets During Multiple Outages to Enhance Power System Security
缓解多次停电期间的饱和割集以增强电力系统安全
- DOI:10.1109/tpwrs.2021.3076973
- 发表时间:2021
- 期刊:
- 影响因子:6.6
- 作者:Sen Biswas, Reetam;Pal, Anamitra;Werho, Trevor;Vittal, Vijay
- 通讯作者:Vittal, Vijay
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Anamitra Pal其他文献
Improving Photovoltaic Hosting Capacity of Distribution Networks with Coordinated Inverter Control - A Case Study of the EPRI J1 Feeder
通过逆变器协调控制提高配电网光伏托管能力——以电科院J1馈线为例
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Dhaval Dalal;Madhura Sondharangalla;Raja Ayyanar;Anamitra Pal - 通讯作者:
Anamitra Pal
Cut-set and Stability Constrained Optimal Power Flow for Resilient Operation During Wildfires
野火期间弹性运行的割集和稳定性约束最佳功率流
- DOI:
10.48550/arxiv.2311.05734 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Satyaprajna Sahoo;Anamitra Pal - 通讯作者:
Anamitra Pal
High-Speed Voltage Control in Active Distribution Systems with Smart Inverter Coordination and Deep Reinforcement Learning
具有智能逆变器协调和深度强化学习的主动配电系统中的高速电压控制
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Mohammad Golgol;Anamitra Pal - 通讯作者:
Anamitra Pal
PMU-Timescale Topology Identification of Sub-station Node-Breaker Models using Deep Learning
使用深度学习对变电站节点断路器模型进行 PMU 时标拓扑识别
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Behrouz Azimian;Anamitra Pal;Backer Abu;Lang Chen;Penn Markham - 通讯作者:
Penn Markham
Anamitra Pal的其他文献
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{{ truncateString('Anamitra Pal', 18)}}的其他基金
CAREER: Time-Synchronized Estimation in Power Systems: Unique Challenges and Innovative Solutions
职业:电力系统中的时间同步估计:独特的挑战和创新的解决方案
- 批准号:
2145063 - 财政年份:2022
- 资助金额:
$ 150万 - 项目类别:
Continuing Grant
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