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
相关论文
共 50 条
  • [1] A Research Review on Application of Artificial Intelligence in Power System Fault Analysis and Location
    He J.
    Luo G.
    Cheng M.
    Liu Y.
    Tan Y.
    Li M.
    1600, Chinese Society for Electrical Engineering (40): : 5506 - 5515
  • [2] A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence
    Burgos, Marcelo Fabian Guato
    Morato, Jorge
    Imacana, Fernanda Paulina Vizcaino
    APPLIED SCIENCES-BASEL, 2024, 14 (03):
  • [3] Application of Artificial Intelligence in PV Fault Detection
    Al-Katheri, Ahmed A.
    Al-Ammar, Essam A.
    Alotaibi, Majed A.
    Ko, Wonsuk
    Park, Sisam
    Choi, Hyeong-Jin
    SUSTAINABILITY, 2022, 14 (21)
  • [4] A Review on Various Artificial Intelligence Techniques Used for Transmission Line Fault Location
    Chakrabarti, Soham
    Chakrabarti, Satarupa
    Swetapadma, Aleena
    PROCEEDINGS OF THE 2018 3RD INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2018), 2018, : 105 - 109
  • [5] Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks
    Rezapour, Hamed
    Jamali, Sadegh
    Bahmanyar, Alireza
    ENERGIES, 2023, 16 (12)
  • [6] Artificial Intelligence and Physics-Based Anomaly Detection in the Smart Grid: A Survey
    Gaggero, Giovanni Battista
    Girdinio, Paola
    Marchese, Mario
    IEEE ACCESS, 2025, 13 : 23597 - 23606
  • [7] Applications of Artificial Intelligence and PMU Data: A Robust Framework for Precision Fault Location in Transmission Lines
    Yuvaraju, V.
    Thangavel, S.
    Golla, Mallikarjuna
    IEEE ACCESS, 2024, 12 : 136565 - 136587
  • [8] Bridging Artificial Intelligence and Railway Cybersecurity: A Comprehensive Anomaly Detection Review
    Qi, Jingwen
    Wang, Jian
    TRANSPORTATION RESEARCH RECORD, 2024,
  • [9] Review on the application of Artificial Intelligence in Antivirus Detection System
    Wang, Xiao-bin
    Yang, Guang-yuan
    Li, Yi-chao
    Liu, Dan
    2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2008, : 9 - 12
  • [10] An anomaly detection on blockchain infrastructure using artificial intelligence techniques: Challenges and future directions - A review
    Chithanuru, Vasavi
    Ramaiah, Mangayarkarasi
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (22)