A Review of Machine Learning Applications in Power System Resilience

被引：0

作者：

Xie, Jian ^{[1
]}

Alvarez-Fernandez, Inalvis ^{[1
]}

Sun, Wei ^{[1
]}

机构：

[1] Univ Cent Florida, Dept Elect & Comp Engn, Orlando, FL 32816 USA

来源：

2020 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM) | 2020年

关键词：

Deep Learning; Machine Learning; Power System Control; Resilience; Restoration; STABILITY ASSESSMENT; PREDICTION; DRIVEN; EXTREME;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

引用

页数：5

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