An Attempt to Investigate the Transformer Failure by using DGA and SFRA Analysis

被引:0
|
作者
Patil, Shubhangi S. [1 ]
Chaudhari, Sushil E. [1 ]
机构
[1] Crompton Greaves Ltd, High Voltage Prod Technol Ctr, Global R&D Ctr, Bombay, Maharashtra, India
关键词
Power transformer; Insulation Deterioration; Overheating; DGA; SFRA; Resonance shift and Failure Analysis;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A sudden increasing power demand leads to manufacture of large number of oil immersed power transformers and other electrical power equipments. Power transformers are the most vital equipment in power system. Any failure in transformer leads to malfunction of whole power system. Unfortunately, the failure rate of these transformers is very high in India, 25% per annum, which is not favorable as compared to international units of 1-2 %. Failures happen due to internal reasons or operational hazardless. Transformer insulation deteriorates as the function of temperature, moisture and time. The core and winding losses, stray losses in tank and metal support structures are the principle sources of heat which cause oil and winding temperature rise. There are multiple reasons for overheating such as improper cooling, excessive eddy currents, bad joints, blocked radiators, overloading, improper earthing and harmonic contents in power supply. This leads to accelerated aging of oil and cellulosic solid insulation, which generate the gases within transformer and further leads to permanent failure. To prevent such failures, effective analysis and diagnosis needs to be investigated. The type of gases generated and amount of gas concentrations in oil efficiently evaluated using Dissolved Gas analysis (DGA). Various other electrical diagnostic tests like winding resistance test, short circuit impedance, oil analysis and sweep frequency response analysis (SFRA) are also helpful for identification of abnormalities and probable fault area. SFRA technique is widely accepted and used for transformer mechanical condition assessment. Based on the type and concentration of gases generated in oil along with application of SFRA test on transformer can help to identify the abnormal areas prior to catastrophic failure. An attempt has made for the investigation on relation of DGA with SFRA response. Case studies are presented here for the transformers which have higher fault gas concentrations (DGA). Additional diagnostic tests and analysis, inspection and history data has found supportive in investigation.
引用
收藏
页数:4
相关论文
共 50 条
  • [21] Condition Assessment of Power Transformer Using SVM based on DGA
    Singh, Jagdeep
    Kaur, Kulraj
    Kumari, Priyam
    Swami, Ankit Kumar
    2016 AL-SADIQ INTERNATIONAL CONFERENCE ON MULTIDISCIPLINARY IN IT AND COMMUNICATION TECHNIQUES SCIENCE AND APPLICATIONS (AIC-MITCSA), 2016,
  • [22] Power transformer fault diagnosis using DGA and artificial intelligence
    Shahrabad S.J.T.
    Ghods V.
    Askari M.T.
    Ghods, Vahid (V.ghods@semnaniau.ac.ir), 1600, Bentham Science Publishers, P.O. Box 294, Bussum, 1400 AG, Netherlands (13): : 579 - 587
  • [23] Comparative study and analysis of DGA methods for transformer mineral oil
    Muhamad, N. A.
    Phung, B. T.
    Blackburn, T. R.
    Lai, K. X.
    2007 IEEE LAUSANNE POWERTECH, VOLS 1-5, 2007, : 45 - 50
  • [24] A Case Study: Transformer Failure Detected with Real-time DGA Monitoring
    Leivo, Senja
    Sattler, Diego
    Duval, Michel
    Chiarella, Carlos
    2023 IEEE ELECTRICAL INSULATION CONFERENCE, EIC, 2023,
  • [25] Improving transformer failure classification on imbalanced DGA data using data-level techniques and machine learning
    Azmi, Putri Azmira R.
    Yusoff, Marina
    Sallehud-din, Mohamad Taufik Mohd
    ENERGY REPORTS, 2025, 13 : 264 - 277
  • [26] Unified Grey relational Analysis on transformer DGA fault diagnosis
    Wang, Q. (wangqz@hqu.edu.cn), 1600, Bentham Science Publishers B.V., P.O. Box 294, Bussum, 1400 AG, Netherlands (08):
  • [27] Comparative Analysis of Transformer Fault Classification Based on DGA Data Using Machine Learning Algorithms
    Coban, Melih
    Fidan, Murat
    Aytar, Oktay
    PROCEEDINGS 2024 IEEE 6TH GLOBAL POWER, ENERGY AND COMMUNICATION CONFERENCE, IEEE GPECOM 2024, 2024, : 263 - 267
  • [28] Fault Identification for Transformer Axial Winding Displacement Using Nanosecond IFRA and SFRA Experiments
    Huang, J. J.
    Tang, W. H.
    Xin, Y. L.
    Zhou, J. J.
    Wu, Q. H.
    2018 3RD ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2018), 2018, 366
  • [29] Experimental Analysis of Short-Circuit Effects in Transformer Winding by Impact Test and SFRA
    Gutten, Miroslav
    Janura, Richard
    Korenciak, Daniel
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON DIAGNOSTIC OF ELECTRICAL MACHINES AND INSULATING SYSTEMS IN ELECTRICAL ENGINEERING (DEMISEE 2016), 2016, : 77 - 80
  • [30] Development of reference SFRA plot of Transformer at Design stage using High Frequency Modelling
    Sharma, Usha
    Chatterjee, Saibal
    Bhuyan, Kaveri
    2012 1ST INTERNATIONAL CONFERENCE ON POWER AND ENERGY IN NERIST (ICPEN), 2012,