The current projection shows that much of the continental U.S. will have significantly hotter and drier days in the following decades, leading to more wildfire hazards that threaten the safety of power grid. Unfortunately, the U.S. power industry is not well prepared and still predominantly relies on empirical fire indices which do not consider the full spectrum of dynamic environmental factors. This paper proposes a new spatio-temporal point process model, Convolutional Non-homogeneous Poisson Process (cNHPP), to quantify wildfire risks for power delivery networks. The proposed model captures both the current short-term and cumulative long-term effects of covariates on wildfire risks, and the spatio-temporal dependency among different segments of the power delivery network. The computation and interpretation of the intensity function are thoroughly investigated, and the connection between cNHPP and Recurrent Neural Network is also discussed. We apply the proposed approach to estimate wildfire risks on major transmission lines in California, utilizing historical fire data, meteorological and vegetation data obtained from the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration. Comparison studies are performed to show the applicability and predictive capability of the proposed approach. Useful insights are obtained that potentially enhance power grid resilience against wildfires.
翻译:目前的预测表明,美国大陆大部分大陆在今后几十年里将出现显著的热热和干燥日,导致更多的野火危害,威胁电网的安全。不幸的是,美国电力工业没有做好充分的准备,而且仍然主要依赖不考虑所有各种动态环境因素的经验性火灾指数。本文建议采用一个新的时空点进程模型,即 " 革命性非同源性Poisson进程 " (CNHPP),以量化电力输送网络的野火风险。拟议的模型捕捉了当前共生效应对野火风险的短期和累积长期影响,以及电力输送网络不同部分的时空依赖性。对强度功能的计算和解释进行了彻底调查,并讨论了CNHAPP与常规神经网络之间的联系。我们运用了拟议方法,利用历史火灾数据、气象和植被数据估算加利福尼亚州主要传输线的野火风险。从国家海洋和大气管理局和国家航空和航天管理局获得的数据,对野火性电网能预测能力进行了比较研究,以显示拟议的电网能预测方法的实用性和预测能力。