基于数据的量化和可视化电力分配系统的弹性指标
Dingwei Wang1, Salish Maharjan1, Junyuan Zheng1
1Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, 50010, USA.
Scientific reports
|January 27, 2026
概括
这项研究使用中断数据和NOAA测量量电网对极端天气的抵御能力. 它开发数据驱动模型来绘制电网漏洞和恢复时间,帮助公用事业规划.
科学领域:
- 电气工程 电气工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 配电电网面临着极端天气事件带来的越来越大的威胁.
- 量化电网弹性对于有效的基础设施管理和公共安全至关重要.
- 现有的方法往往缺乏基于数据的方法来进行本地化的弹性评估.
研究的目的:
- 开发和验证一个数据驱动的框架来量化分布式电网的弹性.
- 以关键指标为关键指标的中断频率和恢复时间来评估弹性.
- 为公用事业规划和基础设施硬化提供可操作的见解.
主要方法:
- 利用历史中断记录和国家海洋和大气管理局 (NOAA) 的天气数据.
- 通过时间和空间聚合,系统地提取中断事件.
- 使用沃罗诺伊多边形划分气候区,并开发以数据驱动的停电脆弱性和恢复时间模型.
主要成果:
- 开发了特定区域的模型,准确地捕捉了天气强度,停电和恢复时间之间的关系.
- 生成了弹性地图,可视化了不同天气区的电网脆弱性.
- 使用来自美国中西部公用事业公司的二十年数据集证明了框架的有效性.
结论:
- 数据驱动的方法为量化电网弹性提供了可靠的方法.
- 弹性地图为优先考虑电网硬化和运营战略提供了宝贵的见解.
- 该框架支持知情决策,以提高电网可靠性以应对极端天气.
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