PSO–SOM Neural Network Algorithm for Series Arc Fault Detection
Joint Authors
Qu, Na
Chen, Jiatong
Zuo, Jiankai
Liu, Jinhai
Source
Advances in Mathematical Physics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-25
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Self-organizing feature map (SOM) neural network is a kind of competitive neural network with unsupervised learning.
It has the strong abilities of self-organization and self-learning.
However, the classification accuracy of SOM neural network may decrease when the features of tested object are not obvious.
In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the weight values of SOM network.
Three indexes, i.e., intra-class density, standard deviation and sample difference, are used to judge the weight value, which can improve the classification accuracy of the SOM network.
PSO–SOM network is applied to the detection of series arc fault in electrical circuits and compared with conventional SOM network and learning vector quantization (LVQ) network.
The detection accuracy of the PSO–SOM network is 95%, which is higher than conventional SOM network and LVQ network.
American Psychological Association (APA)
Qu, Na& Chen, Jiatong& Zuo, Jiankai& Liu, Jinhai. 2020. PSO–SOM Neural Network Algorithm for Series Arc Fault Detection. Advances in Mathematical Physics،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1127464
Modern Language Association (MLA)
Qu, Na…[et al.]. PSO–SOM Neural Network Algorithm for Series Arc Fault Detection. Advances in Mathematical Physics No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1127464
American Medical Association (AMA)
Qu, Na& Chen, Jiatong& Zuo, Jiankai& Liu, Jinhai. PSO–SOM Neural Network Algorithm for Series Arc Fault Detection. Advances in Mathematical Physics. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1127464
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1127464