IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i15p3747-d1445544.html
   My bibliography  Save this article

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

Author

Listed:
  • Shiming Sun

    (Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Yuanhe Tang

    (Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Tong Tai

    (Nari Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Xueyun Wei

    (State Grid Jiangsu Electric Power Co., Ltd., Power Dispatching and Control Center, Nanjing 210024, China)

  • Wei Fang

    (School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China)

Abstract

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.

Suggested Citation

  • Shiming Sun & Yuanhe Tang & Tong Tai & Xueyun Wei & Wei Fang, 2024. "A Review on the Application of Artificial Intelligence in Anomaly Analysis Detection and Fault Location in Grid Indicator Calculation Data," Energies, MDPI, vol. 17(15), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3747-:d:1445544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/15/3747/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/15/3747/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sapountzoglou, Nikolaos & Lago, Jesus & De Schutter, Bart & Raison, Bertrand, 2020. "A generalizable and sensor-independent deep learning method for fault detection and location in low-voltage distribution grids," Applied Energy, Elsevier, vol. 276(C).
    2. Hamed Rezapour & Sadegh Jamali & Alireza Bahmanyar, 2023. "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, MDPI, vol. 16(12), pages 1-18, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sun, Chenhao & Zhou, Zhuoyu & Zeng, Xiangjun & Li, Zewen & Wang, Yuanyuan & Deng, Feng, 2022. "A multi-model-integration-based prediction methodology for the spatiotemporal distribution of vulnerabilities in integrated energy systems under the multi-type, imbalanced, and dependent input data sc," Applied Energy, Elsevier, vol. 320(C).
    2. Hamed Rezapour & Sadegh Jamali & Alireza Bahmanyar, 2023. "Review on Artificial Intelligence-Based Fault Location Methods in Power Distribution Networks," Energies, MDPI, vol. 16(12), pages 1-18, June.
    3. Moamin A. Mahmoud & Naziffa Raha Md Nasir & Mathuri Gurunathan & Preveena Raj & Salama A. Mostafa, 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review," Energies, MDPI, vol. 14(16), pages 1-27, August.
    4. Mojgan Hojabri & Severin Nowak & Antonios Papaemmanouil, 2023. "ML-Based Intermittent Fault Detection, Classification, and Branch Identification in a Distribution Network," Energies, MDPI, vol. 16(16), pages 1-15, August.
    5. Luís Brito Palma, 2024. "Hybrid Approach for Detection and Diagnosis of Short-Circuit Faults in Power Transmission Lines," Energies, MDPI, vol. 17(9), pages 1-30, May.
    6. Yaojing Tang & Yongle Chang & Jinrui Tang & Bin Xu & Mingkang Ye & Hongbo Yang, 2021. "A Novel Faulty Phase Selection Method for Single-Phase-to-Ground Fault in Distribution System Based on Transient Current Similarity Measurement," Energies, MDPI, vol. 14(15), pages 1-19, August.
    7. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:15:p:3747-:d:1445544. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.