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
  • [1] Analysis of Failure in Low-Voltage Terminal Connections and Fault Classification in Power Transformer Using Infrared Thermography
    S. Meradi
    S. Laribi
    S. Bouslimani
    R. Dermouche
    Journal of Failure Analysis and Prevention, 2024, 24 : 547 - 558
  • [2] Bilayered fault detection and classification scheme for low-voltage DC microgrid with weighted KNN and decision tree
    Reddy, O. Yugeswar
    Chatterjee, Soumesh
    Chakraborty, Ajoy Kumar
    INTERNATIONAL JOURNAL OF GREEN ENERGY, 2022, 19 (11) : 1149 - 1159
  • [3] Study On Transformer Winding Deformation Using Low-Voltage Impedance Diagnostic Method
    Zheng, Hanbo
    Pu, Bingjian
    Shao, Yingbiao
    Li, Yuquan
    Wang, Wei
    Xue, Wenchao
    2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, 2015, : 1947 - 1952
  • [4] LoFFT: Low-Voltage FFT Using Lightweight Fault Detection for Energy Efficiency
    Safarpour, Mehdi
    Silven, Olli
    IEEE EMBEDDED SYSTEMS LETTERS, 2023, 15 (03) : 125 - 128
  • [5] A Comparative Analysis of Detecting Bearing Fault, Using Infrared Thermography, Vibration Analysis and Air-Borne Sound
    Athanasopoulos, Nikolaos G.
    Botsaris, Pantelis N.
    ADVANCES IN CONDITION MONITORING OF MACHINERY IN NON-STATIONARY OPERATIONS, 2014, : 171 - 181
  • [6] Aerial infrared thermography for low-cost and fast fault detection in utility-scale PV power plants
    Vidal de Oliveira, Aline Kirsten
    Aghaei, Mohammadreza
    Ruther, Ricardo
    SOLAR ENERGY, 2020, 211 : 712 - 724
  • [7] A condition monitoring and fault detection in the windings of power transformer using impulse frequency response analysis
    Ritesh Kumar
    Adavelli Vaijayanthi
    Ram Deshmukh
    B. Vedik
    Chandan Kumar Shiva
    International Journal of System Assurance Engineering and Management, 2022, 13 : 2062 - 2074
  • [8] A condition monitoring and fault detection in the windings of power transformer using impulse frequency response analysis
    Kumar, Ritesh
    Vaijayanthi, Adavelli
    Deshmukh, Ram
    Vedik, B.
    Shiva, Chandan Kumar
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2022, 13 (04) : 2062 - 2074
  • [9] Fast Fault Detection and Isolation in Low-Voltage DC Microgrids Using Fuzzy Inference System
    Abdali, Ali
    Mazlumi, Kazem
    Noroozian, Reza
    2017 5TH IRANIAN JOINT CONGRESS ON FUZZY AND INTELLIGENT SYSTEMS (CFIS), 2017, : 172 - 177
  • [10] Fault Prediction for Power Transformer Using Optical Spectrum of Transformer Oil and Data Mining Analysis
    Fauzi, Nur Afini
    Ali, N. H. Nik
    Ker, Pin Jern
    Thiviyanathan, Vimal Angela
    Leong, Yang Sing
    Sabry, Ahmad H.
    Bin Jamaludin, Md Zaini
    Lo, Chin Kim
    Mun, Looe Hui
    IEEE ACCESS, 2020, 8 : 136374 - 136381