Review on Supervised and Unsupervised Learning Techniques for Electrical Power Systems: Algorithms and Applications

被引：11
作者：
Chen, Songbo [1 ,2 ]
机构：
[1] Zhejiang Univ, Coll Elect Engn, Hangzhou 511500, Zhejiang, Peoples R China
[2] Qingyuan Power Supply Bur, Qingyuan, Peoples R China
来源：
IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING | 2021年 / 16卷 / 11期
关键词：
machine learning; supervised learning; unsupervised learning; generation; transmission; distribution; DEEP BELIEF NETWORK; CONVOLUTIONAL NEURAL-NETWORK; RANDOM-FOREST MODEL; FAULT-DIAGNOSIS; BIG DATA; PREDICTION; MACHINES; REPRESENTATIONS; IDENTIFICATION; EXTRACTION;
D O I：
10.1002/tee.23452
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：
Machine learning (ML) has become a rising sophisticated technological application trend in the electrical industry in recent years. Such innovation provides optional methodologies for many existing applications, such as power and load profile forecasting, reliability evaluation, substation behavior detection and state observation of electrical equipment, and so on. This paper presents a review of various supervised and unsupervised ML techniques and applications for electrical power systems, including generation, transmission, distribution and micro-grid. The algorithms and applications are mainly summarized from IEEE journals and the interest of this paper shows the roles and developments of most used algorithms and its corresponding extensions and performance in different applications. (c) 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
引用
收藏
页码：1487 / 1499
页数：13
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