Analysis of Failure in Low-Voltage Terminal Connections and Fault Classification in Power Transformer Using Infrared Thermography

被引:0
作者
Meradi, S. [1 ]
Laribi, S. [2 ]
Bouslimani, S. [3 ]
Dermouche, R. [1 ]
机构
[1] ENSTA Algiers, Lab Innovat Technol, COSI Team, Algiers, Algeria
[2] Univ Ibn Khaldoun, Dept Elect Engn, Lab L2GEGI, Tiaret, Algeria
[3] Higher Natl Sch Renewable Energy Environm & Sustai, Environm & Sustainable Dev, Batna, Algeria
关键词
Power transformers; Condition monitoring; Fault classification; Infrared thermography; Thermal imaging; Fault detection;
D O I
10.1007/s11668-024-01857-2
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This paper presents a comprehensive analysis of failures in low-voltage terminal connections within power transformers and proposes a fault classification methodology based on infrared thermography (IRT). Low-voltage terminal connections play a critical role in the reliable operation of power transformers, and their failures can lead to severe operational issues. In this study, we employ IRT as a noninvasive and efficient diagnostic tool to identify and classify various types of failures, including loose connections, overheating, and corrosion. The research involves the collection of infrared thermograms (IRT images) from the low-voltage terminals of power transformers under different operating conditions. The proposed methodology demonstrates its effectiveness in detecting and classifying low-voltage terminal connection failures, thereby enabling timely preventive maintenance and minimizing the risk of transformer malfunctions. This research contributes to enhancing the reliability and longevity of power transformers, reducing downtime, and optimizing maintenance practices in the power industry.
引用
收藏
页码:547 / 558
页数:12
相关论文
共 27 条
  • [21] Low-Voltage Alternating Current Series Arc Fault Detection Using Periodic Background Subtraction and Linear Dividing Lines
    Zhang, Xiaofei
    Li, Jinjie
    Han, Bangzheng
    Wang, Wei
    Zou, Guofeng
    IEEE ACCESS, 2025, 13 : 47201 - 47216
  • [22] Short-Circuit Fault Detection and Isolation Using Filter Capacitor Current Signature in Low-Voltage DC Microgrid Applications
    Yadav, Neelesh
    Tummuru, Narsa Reddy
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2022, 69 (08) : 8491 - 8500
  • [23] Conventional methods of dissolved gas analysis using oil-immersed power transformer for fault diagnosis: A review
    Ali, Mohd Syukri
    Bakar, Ab Halim Abu
    Omar, Azimah
    Jaafar, Amirul Syafiq Abdul
    Mohamed, Siti Hajar
    ELECTRIC POWER SYSTEMS RESEARCH, 2023, 216
  • [24] Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier
    Fanchiang, Kuo-Hao
    Huang, Yen-Chih
    Kuo, Cheng-Chien
    ELECTRONICS, 2021, 10 (10)
  • [25] A Semi-Supervised Autoencoder With an Auxiliary Task (SAAT) for Power Transformer Fault Diagnosis Using Dissolved Gas Analysis
    Kim, Sunuwe
    Jo, Soo-Ho
    Kim, Wongon
    Park, Jongmin
    Jeong, Jingyo
    Han, Yeongmin
    Kim, Daeil
    Youn, Byeng Dong
    IEEE ACCESS, 2020, 8 : 178295 - 178310
  • [26] Fault Classification in Power Distribution Systems Using Multiresolution Analysis and a Fuzzy-ARTMAP Neural NetworkAnalysis and a Fuzzy-ARTMAP Neural Network
    Bernardes, H. R. S.
    Tonelli-Neto, M. S.
    Minussi, C. R.
    IEEE LATIN AMERICA TRANSACTIONS, 2021, 19 (11) : 1824 - 1831
  • [27] Risk Assessment by Using Failure Modes and Effects Analysis (FMEA) Based on Power Transformer Aging for Maintenance and Replacement Decision
    Eyuboglu, Onur Hakki
    Dindar, Burak
    Gul, Omer
    2020 IEEE 2ND GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE (IEEE GPECOM2020), 2020, : 251 - 255