Open AccessTEM apparent resistivity imaging for grounding grid detection using artificial neural network
Author(s) -
Qin Shanqiang,
Wang Yao,
Tai HengMing,
Wang Haowen,
Liao Xian,
Fu Zhihong
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6450
Subject(s) - ground , artificial neural network , electrical resistivity and conductivity , computer science , artificial intelligence , grid , geology , electrical engineering , engineering , geodesy
Transient electromagnetic (TEM) method is a reliable means of non‐destructive testing for fault diagnosis of grounding grid in substation. In view of the small transmitter loop of the TEM system and many measuring points and survey lines of grounding grid detection in substation. This study presents a fast solution to TEM apparent resistivity imaging for grounding grid detection using artificial neural networks. The input ‐output mapping relation of neural network is established based on TEM response characteristics of the grounding grid. The built network could map the recorded TEM data of grounding grid detection and quickly obtain the resistivity image. The proposed method offers accuracy and fast computation for the resistivity imaging of grounding grid. Feasibility and technical attractiveness of the proposed method in fast imaging of apparent resistivity is investigated with the measurement of grounding grids in the actual substations. It can be used in real time so that the recorded TEM data in substation can be calculated without re‐training, which avoids time‐consuming inversion computation. The rapid processing of massive data and submitting the detection results of the grounding grid to customers rapidly and in real time, which is expectation for a modern power industry.