Grounding grid corrosion detection based on mini-batch gradient descent and greedy method
| Autor: | Hu Jiayuan, Hongpeng Xie, Liu Sen, Hua Mingsheng, He Yifan, Fan Yang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Artificial neural network Heuristic (computer science) Computer science Physics QC1-999 Process (computing) General Physics and Astronomy 02 engineering and technology 021001 nanoscience & nanotechnology Fault (power engineering) 01 natural sciences Gröbner basis Robustness (computer science) 0103 physical sciences 0210 nano-technology Gradient descent Greedy algorithm Algorithm |
| Zdroj: | AIP Advances, Vol 11, Iss 6, Pp 065034-065034-11 (2021) |
| ISSN: | 2158-3226 |
| DOI: | 10.1063/5.0051678 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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