Energy Entropy and Particle Swarm Optimization BP Neural Network of Fault Diagnosis Techniques of Coal Mine Cable

Authors

Zhiling Ren, Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China;
Yuanyuan Zhang, Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China;

Abstract

Abstract: Aimed at solving the problem of the type of fault difficult to identification when power feeder of coal mine occurred single-phase ground fault, in order to ensure coal mines production safety, a method of fault diagnosis based on wavelet packet energy entropy (WP-EE) and combined with particle swarm optimization neural network was proposed. The type of cable fault was simulated by Matlab, the acquired post-fault voltage signal was performed the three layers wavelet Packet decomposition, the fault characteristic signals was divided into eight segments by frequency, characteristics calculated the entropy energy spectrum according to the information entropy theory, PSO neural network model was constructed, spectrum entropy signal as to the characteristics of the input vector achieved entropy feature vector classification. Experimental results also show that the method for fault diagnosis of cable mine is feasible, which can detect cable faults quickly and efficiently.

Recommended Citation

Ren, Zhiling and Zhang, Yuanyuan (2020) "Energy Entropy and Particle Swarm Optimization BP Neural Network of Fault Diagnosis Techniques of Coal Mine Cable," Journal of System Simulation: Vol. 27: Iss. 5, Article 18.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss5/18

First Page

1044

Revised Date

2014-09-23

DOI Link

https://doi.org/

Last Page

1049

CLC

TP139.19

Recommended Citation

Ren Zhiling, Zhang Yuanyuan. Energy Entropy and Particle Swarm Optimization BP Neural Network of Fault Diagnosis Techniques of Coal Mine Cable[J]. Journal of System Simulation, 2015, 27(5): 1044-1049.