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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Learning Geotemporal Nonstationary Failure and Recovery of Power Distribution
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Learning Geotemporal Nonstationary Failure and Recovery of Power Distribution

机译：学习地时非平稳故障和配电的恢复

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摘要

Smart energy grid is an emerging area for new applications of machine learning in a nonstationary environment. Such a nonstationary environment emerges when large-scale failures occur at power networks because of external disruptions such as hurricanes and severe storms. Power distribution networks lie at the edge of the grid, and are especially vulnerable to external disruptions. Quantifiable approaches are lacking and needed to learn nonstationary behaviors of large-scale failure and recovery of power distribution. This paper studies such nonstationary behaviors in three aspects. First, a novel formulation is derived for an entire life cycle of large-scale failure and recovery of power distribution. Second, spatial-temporal models of failure and recovery of power distribution are developed as geolocation-based multivariate nonstationary $GI(t)/G(t)/infty$ queues. Third, the nonstationary spatial-temporal models identify a small number of parameters to be learned. Learning is applied to two real-life examples of large-scale disruptions. One is from Hurricane Ike, where data from an operational network is exact on failures and recoveries. The other is from Hurricane Sandy, where aggregated data is used for inferring failure and recovery processes at one of the impacted areas. Model parameters are learned using real data. Two findings emerge as results of learning: 1) failure rates behave similarly at the two different provider networks for two different hurricanes but differently at the geographical regions and 2) both the rapid and slow-recovery are present for Hurricane Ike but only slow recovery is shown for a regional distribution network from Hurricane Sandy.
机译：智能能源网格是在非平稳环境中机器学习新应用的新兴领域。当由于外部干扰（例如飓风和严重暴风雨）导致电网发生大规模故障时，就会出现这种非平稳环境。配电网络位于电网边缘，特别容易受到外部干扰。缺乏可量化的方法，需要学习大量故障和配电恢复的非平稳行为。本文从三个方面研究了这种非平稳行为。首先，针对大规模故障和配电恢复的整个生命周期推导了一种新颖的公式。其次，将故障时变和配电恢复的时空模型开发为基于地理位置的多元非平稳$ GI（t）/ G（t）/ infty $队列。第三，非平稳的时空模型确定了少量要学习的参数。学习应用于两个大规模破坏的现实例子。一种是来自艾克飓风，那里来自运营网络的数据准确地反映了故障和恢复情况。另一个来自飓风桑迪，其中汇总的数据用于推断受影响区域之一的故障和恢复过程。使用实际数据学习模型参数。学习的结果有两个发现：1）对于两个不同的飓风，故障率在两个不同的提供者网络上表现相似，但在地理区域上则不同； 2）艾克飓风既有快速恢复又有缓慢恢复，但只有缓慢恢复针对飓风桑迪的区域分销网络显示。

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