Grounding grid corrosion detection based on mini-batch gradient descent and greedy method

To ensure the speed, recall, and precision of the algorithm to solve the algorithm’s measurement failure due to the problem of high underdetermination and the deviation of some outgoing lines from accessible nodes, the ideal resistance method is proposed to diagnose the corrosion of the grounding gr...

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Bibliographic Details
Published in:AIP advances Vol. 11; no. 6; pp. 065034 - 065034-11
Main Authors: Xie, Hongpeng, Yang, Fan, Hua, Mingsheng, Liu, Sen, Hu, Jiayuan, He, Yifan
Format: Journal Article
Language:English
Published: Melville American Institute of Physics 01.06.2021
AIP Publishing LLC
Subjects:
Accessibility
Algorithms
Corrosion resistance
Deviation
Electrical grounding
Failure
Fault diagnosis
Information retrieval
Multiple objective analysis
Neural networks
Nodes
Optimization
Rings (mathematics)
Training
ISSN:2158-3226, 2158-3226
Online Access:Get full text
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Summary:To ensure the speed, recall, and precision of the algorithm to solve the algorithm’s measurement failure due to the problem of high underdetermination and the deviation of some outgoing lines from accessible nodes, the ideal resistance method is proposed to diagnose the corrosion of the grounding grid. The information extraction algorithm, a two-way heuristic network resistance diagnosis method, is added. A non-linear multi-objective optimization model of the deviation between the measured potential and the real potential is established. This paper puts forward that the grounding network’s fault diagnosis is analogous to a neural network’s training process, which makes full use of the excellent robustness and rapidity of a neural network training method. Simultaneously, the algorithm adds the retraining method combined with the field excavation in training and proves its feasibility through the Grobner basis codimension theory in algebraic geometry. Through result verification, it is found that the new algorithm can avoid the inversion failure caused by the algorithm failure due to the lack of accessible nodes and the abnormal part of input data at the same time as a fast solution.
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ISSN:2158-3226
2158-3226
DOI:10.1063/5.0051678