Dissolved Gas Analysis of Mineral Oil for Power Transformer Fault Diagnosis Using Fuzzy Logic

被引:3
|
作者
Huang, Yann-Chang [1 ]
Sun, Huo-Ching [1 ]
机构
[1] Cheng Shiu Univ, Dept Elect Engn, Kaohsiung, Taiwan
关键词
Dissolved gas analysis; power transformer; fuzzy logic; INCIPIENT FAULTS; SYSTEM;
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
This paper reviews the use of fuzzy logic (FL) for dissolved gas analysis (DGA) of mineral oil for power transformer fault diagnosis (PTFD). A brief overview of conventional PTFD techniques using DGA of mineral oil is firstly surveyed. Then, applications of FL techniques for PTFD reported in international journals for evaluating power transformer conditions are extensively reviewed. Various FL techniques for PTFD have been developed to reduce operating costs, enhance operational reliability, and improve power and services supplied to customers. These FL techniques enable researchers to analyze fault phenomena and diagnose transformer faults, and these approaches have evolved rapidly as highly effective approaches for PTFD. Potential improvements to FL-based systems are also discussed. Our conclusion is that no single DGA technique enables detection of the full range of faults, which is needed for reliable assessment of all power transformer conditions. Therefore, the most effective PTFD technique is to combine outputs from various DGA diagnostic methods and to aggregate them into an overall evaluation.
引用
收藏
页码:974 / 981
页数:8
相关论文
共 50 条
  • [21] Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data
    Li, Enwen
    Wang, Linong
    Song, Bin
    Jian, Siliang
    [J]. ENERGIES, 2018, 11 (09)
  • [22] Transformer fault diagnosis using Dissolved Gas Analysis technology and Bayesian networks
    Lakehal, A.
    Ghemari, Z.
    Saad, S.
    [J]. 2015 4TH INTERNATIONAL CONFERENCE ON SYSTEMS AND CONTROL (ICSC), 2015, : 194 - 198
  • [23] Maximum Likelihood Classification for Transformer Fault Diagnosis Using Dissolved Gas Analysis
    Sreelakshmi, S. M.
    Tharamal, Lakshmi
    Preetha, P.
    [J]. 2021 IEEE ELECTRICAL INSULATION CONFERENCE (EIC), 2021, : 381 - 384
  • [24] Power transformer fault diagnosis based on dissolved gas analysis by support vector machine
    Bacha, Khmais
    Souahlia, Seifeddine
    Gossa, Moncef
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2012, 83 (01) : 73 - 79
  • [25] Fuzzy Logic Based Analysis of Dissolved Decay Contents in Transformer Oil
    Ghosh, Nitika
    Bhadoria, Vikas Singh
    Sharma, Ram Naresh
    Shrivastava, Vivek
    [J]. Springer Series in Reliability Engineering, 2020, : 361 - 385
  • [26] Transformer fault diagnosis using fuzzy logic and neural network
    Kalavathi, MS
    Reddy, BR
    Singh, BP
    [J]. 2005 ANNUAL REPORT CONFERENCE ON ELECTRICAL INSULATION AND DIELECTRIC PHENOMENA, 2005, : 486 - 489
  • [27] Incipient Fault Diagnosis of Transformer by DGA Using Fuzzy Logic
    Apte, Sandip
    Somalwar, Rahul
    Wajirabadkar, Ashwini
    [J]. 2018 IEEE INTERNATIONAL CONFERENCE ON POWER ELECTRONICS, DRIVES AND ENERGY SYSTEMS (PEDES), 2018,
  • [28] Power transformer fault type estimation using artificial neural network based on dissolved gas in oil analysis
    Gunes, Ibrahim
    Gozutok, Abdulkadir
    Ucan, Osman Nuri
    Kiremitci, Baris
    [J]. ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS, 2009, 17 (04): : 193 - 198
  • [29] A case-based reasoning approach to power transformer fault diagnosis using dissolved gas analysis data
    Qian, Z.
    Gao, W. S.
    Wang, F.
    Yan, Z.
    [J]. EUROPEAN TRANSACTIONS ON ELECTRICAL POWER, 2009, 19 (03): : 518 - 530
  • [30] Application of Fuzzy Logic in Fault Diagnosis in Transformers using Dissolved Gas based on Different Standards
    Hooshmand, Rahmatollah
    Banejad, Mahdi
    [J]. PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 17, 2006, 17 : 157 - +