Artificial Intelligence Techniques for Stability Analysis in Modern Power Systems

被引:2
作者
Fang, Jiashu [1 ]
Liu, Chongru [2 ]
机构
[1] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 610054, Peoples R China
[2] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewable, Beijing 102206, Peoples R China
来源
IENERGY | 2024年 / 3卷 / 04期
基金
中国国家自然科学基金;
关键词
Smart grids; artificial intelligence; deep learning; stability analysis; GENERATIVE ADVERSARIAL NETWORKS; DYNAMIC SECURITY ASSESSMENT; LEARNING-MACHINE; STATE ESTIMATION; PREDICTION; FRAMEWORK; DECISION; ENHANCEMENT; ACCURACY; FAILURE;
D O I
10.23919/IEN.2024.0027
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
Effective stability analysis is essential for the secure operation of modern power systems. As smart grids evolve with increased interconnection, renewable energy integration, and electrification, the large-scale deployment of ultra-high voltage AC/DC networks introduces various operational modes and potential fault points, posing significant challenges to maintaining stability. Traditional analysis and control methods fall short under these conditions. In contrast, emerging artificial intelligence (AI) techniques, combined with real-time data collection, provide powerful tools for enhancing stability analysis in smart grids. This paper comprehensively explores AI techniques in stability analysis, discussing the necessity and rationale for integrating AI into stability analysis through the lenses of knowledge fusion, discovery, and adaptation. It provides a thorough review of current studies on AI applications in stability analysis, addresses key challenges, and outlines future prospects for AI integration, highlighting its potential to improve analytical capabilities in complex power systems.
引用
收藏
页码:194 / 215
页数:22
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