A Review on the Application of Artificial Intelligence in Anomaly Analysis Detection and Fault Location in Grid Indicator Calculation Data

被引:0
|
作者
Sun, Shiming [1 ]
Tang, Yuanhe [1 ]
Tai, Tong [1 ]
Wei, Xueyun [2 ]
Fang, Wei [3 ]
机构
[1] Nari Grp Corp, State Grid Elect Power Res Inst, Nanjing 211106, Peoples R China
[2] State Grid Jiangsu Elect Power Co Ltd, Power Dispatching & Control Ctr, Nanjing 210024, Peoples R China
[3] Nanjing Univ Informat Sci & Technol, Sch Comp Sci, Nanjing 210044, Peoples R China
关键词
artificial intelligence; power grid data; anomaly analysis detection; fault location; deep learning; NEURAL-NETWORKS;
D O I
10.3390/en17153747
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
With the rapid development of artificial intelligence (AI), AI has been widely applied in anomaly analysis detection and fault location in power grid data and has made significant research progress. Through looking back on traditional methods and deep learning methods in anomaly analysis detection and fault location of power grid data, we aim to provide readers with a comprehensive understanding of the existing knowledge and research advancements in this field. Firstly, we introduce the importance of anomaly analysis detection and fault location in power grid data for the safety and stability of power system operations and review traditional methods for anomaly analysis detection and fault location in power grid data, analyzing their advantages and disadvantages. Next, the paper briefly introduces the concepts of commonly used deep learning models in this field and explores, in depth, the application of deep learning methods in anomaly analysis detection and fault location of power grid data, summarizes the current research progress, and highlights the advantages of deep learning over traditional methods. Finally, we summarize the current issues and challenges faced by deep learning in this field and provide an outlook on future research direction.
引用
收藏
页数:15
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