An EEMD-Based Electromagnetic Induction Method for Nondestructive Testing of Buried Metal Conductors

被引:8
作者
Song, Hengli [1 ,2 ,3 ]
Dong, Haobin [1 ,2 ,3 ]
Yuan, Zhiwen [3 ]
Zhu, Jun [3 ]
Zhang, Haiyang [3 ]
Huang, Yujin [1 ,2 ]
机构
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Hubei, Peoples R China
[3] Sci & Technol Near Surface Detect Lab, Wuxi 214035, Jiangsu, Peoples R China
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Conductors; Grounding; Electromagnetic interference; Metals; Substations; Corrosion; Magnetic resonance imaging; Grounding grid; electromagnetic induction (EMI); ensemble empirical mode decomposition (EEMD); single channel blind source separation (SCBSS); nondestructive testing (NDT); EMPIRICAL MODE DECOMPOSITION; DIAGNOSIS;
D O I
10.1109/ACCESS.2019.2944549
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Nondestructive testing of substation grounding grids is an issue that has increasing importance. The traditional EMI method transforms the condition of the undergrounding conductors to the surficial induced electric signal in the sensing coil. However, The EMI signals excited by multiple coexisted faults combining with other unknown noises surrounding the substation often cause the failure of detection. Therefore, the observed EMI signals rather complex and cannot be used directly. To address this problem, the separation of individual signatures from the mixture is posed as an SCBSS problem. To extract the induced signal, an EEMD-based EMI method is proposed. The desired signal is then reconstructed to visualize the structure of the grounding grids by a virtual instrument that consists of DAQ and digital signal processing modules. The numerical simulation and practical experiments are employed. The results show the proposed method can be used to effectively detect the topological structure of grounding grid in real substations electromagnetic environment.
引用
收藏
页码:142261 / 142271
页数:11
相关论文
共 29 条
  • [1] Estimation of seasonal variation of ground resistance using Artificial Neural Networks
    Asimakopoulou, Fani E.
    Tsekouras, George J.
    Gonos, Ioannis F.
    Stathopulos, Ioannis A.
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2013, 94 : 113 - 121
  • [2] Blind Separation of Cyclostationary Sources Sharing Common Cyclic Frequencies Using Joint Diagonalization Algorithm
    Brahmi, Amine
    Ghennioui, Hicham
    Corbier, Christophe
    Guillet, Francois
    Lahbabi, M'hammed
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2017, 2017
  • [3] Fan Y., 2018, IEEE T MAGN, V54
  • [4] Automatic Defect Identification of Eddy Current Pulsed Thermography Using Single Channel Blind Source Separation
    Gao, Bin
    Bai, Libing
    Woo, Wai Lok
    Tian, Gui Yun
    Cheng, Yuhua
    [J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2014, 63 (04) : 913 - 922
  • [5] Gomes L. V., 2016, INT J ENG SCI INVENT, V7, P24
  • [6] Kou XK, 2018, PROG ELECTROMA RES M, V67, P105
  • [7] Application of the EEMD method to rotor fault diagnosis of rotating machinery
    Lei, Yaguo
    He, Zhengjia
    Zi, Yanyang
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2009, 23 (04) : 1327 - 1338
  • [8] A weighted multi-scale morphological gradient filter for rolling element bearing fault detection
    Li, Bing
    Zhang, Pei-lin
    Wang, Zheng-jun
    Mi, Shuang-shan
    Liu, Dong-sheng
    [J]. ISA TRANSACTIONS, 2011, 50 (04) : 599 - 608
  • [9] Topological measurement and characterization of substation grounding grids based on derivative method
    Li Chunli
    He Wei
    Yao Degui
    Yang Fan
    Kou Xiaokuo
    Wang Xiaoyu
    [J]. INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2014, 63 : 158 - 164
  • [10] Transmission Line Ice Coating Prediction Model Based on EEMD Feature Extraction
    Li, Hongyan
    Chen, Yunliang
    Zhang, Guojun
    Li, Jianxin
    Zhang, Nian
    Du, Bo
    Liu, Hao
    Xiong, Naixue
    [J]. IEEE ACCESS, 2019, 7 : 40695 - 40706