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arXiv 2501.04830eess.SYcs.SY
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基于深度学习的电力系统韧性评估方法

A Deep Learning-Based Method for Power System Resilience Evaluation

Wayne State University(韦恩州立大学)

Xuesong Wang, Caisheng Wang

AI总结:

本文提出一种基于深度学习的电力系统韧性评估方法,通过整合历史停电和天气数据预测事件级韧性,并用于评估实际电力系统的韧性,以指导投资提升脆弱区域的韧性。

摘要:

电力系统韧性对现代社会至关重要,因为极端天气引起的停电可能严重扰乱社区。现有的基于统计和模拟的方法在量化韧性方面要么是回顾性的,要么依赖于简化的物理模型,限制了它们的应用性。本文提出了一种基于深度学习的框架,整合历史停电和天气数据来预测事件级韧性,使用韧性梯形法进行测量。训练好的模型随后应用于基准天气数据集来估算区域韧性,当政策制定者希望强调特定人口群体的需求时,可以将社会经济和人口因素作为加权项纳入其中。该框架的有效性首先在模拟停电记录上得到验证,显示出预测值与模拟韧性值之间的一致性。然后将其应用于真实的历史停电数据来评估实际电力系统的韧性。除了评估外,结果还可以指导针对分布式能源资源的定向投资,以提高易受攻击地区的韧性。

英文摘要:

Power system resilience is vital to modern society, as outages caused by extreme weather can severely disrupt communities. Existing statistical and simulation-based methods for resilience quantification are either retrospective or rely on simplified physical models, limiting their applicability. This paper proposes a deep learning-based framework that integrates historical outage and weather data to predict event-level resilience, measured using the resilience trapezoid method. The trained model is then applied to a benchmark weather dataset to estimate regional resilience, with optional socioeconomic and demographic factors incorporated as weighting terms when policymakers wish to emphasize the needs of specific population groups. The effectiveness of the framework is first validated on simulated outage records, showing strong agreement between predicted and simulated resilience values. It is then applied to real historical outage data to assess the resilience of actual power systems. Beyond evaluation, the results can guide targeted investments in distributed energy resources to improve resilience in vulnerable regions.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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