Machine Learning Based Power Grid Outage Prediction in Response to Extreme Events

被引：117

作者：

Eskandarpour, Rozhin ^{[1
]}

Khodaei, Amin ^{[1
]}

机构：

[1] Univ Denver, Dept Elect & Comp Engn, Denver, CO 80210 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2017年 / 32卷 / 04期

基金：

美国国家科学基金会;

关键词：

Extreme events; machine learning; power system resilience; ARTIFICIAL NEURAL-NETWORK;

D O I：

10.1109/TPWRS.2016.2631895

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A machine learning based prediction method is proposed in this paper to determine the potential outage of power grid components in response to an imminent hurricane. The decision boundary, which partitions the components' states into two sets of damaged and operational, is obtained via logistic regression by using a second-order function and proper parameter fitting. Two metrics are examined to validate the performance of the obtained decision boundary in efficiently predicting component outages.

引用

页码：3315 / 3316

页数：2

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