The non-excavation corrosion prediction model of grounding grid based on particle swarm optimization extreme learning machine. Issue 2 (March 2021)

• Record Type: Journal Article
• Authors: Li, Wenbin; Wang, Yong; Feng, Yanting; Wang, Qing; Xu, Xuexia; Li, Guowei; Dong, Guozhen; Jing, Shangqian; Chen, Ersong; Fan, Xiaoliang; Liu, Jianmin
• Is Part Of: IOP conference series. Volume 692:Issue 2(2021)
• Publication Date: 2021-03
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/692/2/022118
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: The grounding grid is an indispensible part in the eletrical system. Nevertheless, the grounding grid materials are susceptible. Considering the small sample size and strong nonlinear feature of the grounding grid corrosion, we introduced a non-excavation corrosion prediction model based on particle swarm optimization extreme learning machine. This model utilized the extreme learning machine to fast deal with the nonlinear relationship, and utilized the particle swarm optimization to search global optimal solution. Compared with generalized regression neural network and BP neural network, the prediction results of this model are more accurate. Thus, this model might have bright future in improving the accuracy of corrosion prediction of grounding grids.