Machine learning for power system protection and control

被引：0
|
作者：
Yang H. [1 ]
Liu X. [2 ]
Zhang D. [1 ]
Chen T. [1 ]
Li C. [3 ]
Huang W. [3 ]
机构：
[1] College of Electrical and Information Engineering, Hunan University, Changsha
[2] School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[3] School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai
来源：
Electricity Journal | 2021年 / 34卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Deep learning; Fault diagnosis; Machine learning; Power system;
D O I：
10.1016/j.tej.2020.106881
中图分类号：
学科分类号：
摘要：
Since the power system is undergoing a transition into a more flexible and complex system, it urges improvements in fault diagnosis techniques for the power system protection to avoid cascading damages at the occurrence of faults. Facing with challenges of massive data, several machine-learning based methods for identifying faults were proposed over the past years. In this paper, an overview of conventional and trending machine learning applications for the fault diagnosis are summarized. © 2020
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