Application of Graphene Hybrid Materials in Fault Characteristic Gas Detection of Oil-Immersed Equipment

被引:8
|
作者
Jin, Lingfeng [1 ,2 ,3 ]
Chen, Weigen [1 ,2 ]
Zhang, Ying [3 ]
机构
[1] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing, Peoples R China
[2] Chongqing Univ, Sch Elect Engn, Chongqing, Peoples R China
[3] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
来源
FRONTIERS IN CHEMISTRY | 2018年 / 6卷
基金
中国国家自然科学基金;
关键词
graphene; gas sensor; oil-immersed equipment; sensing mechanism; fault characteristic gas; SENSING PROPERTIES; CARBON-MONOXIDE; AG NANOPARTICLES; OXIDE; ZNO; NANOCOMPOSITE; NANOSTRUCTURES; COMPOSITES; SENSORS; LAYER;
D O I
10.3389/fchem.2018.00399
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Graphene and its hybrid materials, due to their unique structures and properties, have attracted enormous attention for both fundamental and applied research in the gas sensing field. This review highlights the recent advances in the application of graphene-based gas sensors in fault characteristic gas detection of oil-immersed equipment, which can effectively achieve condition monitoring of the oil-immersed power equipment. In this review, the synthetic methods of graphene hybrid materials with noble metals, metal oxides and their combination are presented. Then, the basic sensing mechanisms of graphene hybrid materials and gas sensing properties of graphene hybrid materials sensors to hydrogen (H-2), carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), acetylene (C2H2), ethylene (C2H4), and ethane (C2H6), which are the fault characteristic gas in oil-immersed power equipment, are summarized. Finally, the future challenges and prospects of graphene hybrid materials gas sensors in this field are discussed.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Application of Dissolved Gas Analysis in Assessing Degree of Healthiness or Faultiness with Fault Identification in Oil-Immersed Equipment
    Irungu, George Kimani
    Akumu, Aloys Oriedi
    ENERGIES, 2020, 13 (18)
  • [2] Application of fuzzy equivalent matrix for fault diagnosis of oil-immersed insulation
    Zhang, Guanjun
    Yasuoka, Koichi
    Ishii, Shozo
    IEEE International Conference on Conduction and Breakdown in Dielectric Liquids, ICDL, 1999, : 400 - 403
  • [3] Fault Detection Method of Oil-immersed Transformer Based on Thermal Imaging
    Song, Xi
    Zhang, Mingdong
    Xie, Weidong
    Cao, Shaorong
    Gao, Chaochao
    Journal of Computers (Taiwan), 2024, 35 (05) : 35 - 46
  • [4] Study on Fault Detection Robot for Oil-immersed Transformer based on WiFi control
    Huang, Ronghui
    Zhao, Yuming
    Li, Xun
    Feng, Yingbin
    Wu, Guoxing
    Li, Zhigang
    2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 50 - 55
  • [5] Quick oil leakage repair method for oil-immersed equipment in substation
    Komatsu, Y.
    Watanabe, T.
    Kawakita, K.
    2013 CIGRE Auckland Symposium, 2013, 2013-September
  • [6] A Hybrid machine-learning method for oil-immersed power transformer fault diagnosis
    Yang, Xiaohui
    Chen, Wenkai
    Li, Anyi
    Yang, Chunsheng
    IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2020, 15 (04) : 501 - 507
  • [7] Application of Dielectric Response for Oil-immersed Transformer
    Yang, Shuangsuo
    Dong, Ming
    Zhang, Guanjun
    Zhang, Zhi
    ICPADM 2009: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON PROPERTIES AND APPLICATIONS OF DIELECTRIC MATERIALS, VOLS 1-3, 2009, : 370 - +
  • [8] A Hybrid Least-square Support Vector Machine Approach to Incipient Fault Detection for Oil-immersed Power Transformer
    Wei, C. H.
    Tang, W. H.
    Wu, Q. H.
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2014, 42 (05) : 453 - 463
  • [9] Data augmentation for fault diagnosis of oil-immersed power transformer
    Li, Ke
    Li, Jian
    Huang, Qi
    Chen, Yuhui
    ENERGY REPORTS, 2023, 9 : 1211 - 1219
  • [10] Intelligent Fault Types Diagnostic System for Dissolved Gas Analysis of Oil-immersed Power Transformer
    Yang, Ming-Ta
    Hu, Li-Siang
    IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION, 2013, 20 (06) : 2317 - 2324