Fault Diagnosis Technology for Ship Electrical Power System

Chaochun Yu, Liang Qi, Jie Sun, Chunhui Jiang, Jun Su and Wentao Shu
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Chaochun Yu: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Liang Qi: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Jie Sun: School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Chunhui Jiang: Electrical Design Department, Zhenjiang Hongye Science & Technology Co., Ltd., Zhenjiang 212000, China
Jun Su: Mechanical Design Department, Zhenjiang Hongye Science & Technology Co., Ltd., Zhenjiang 212000, China
Wentao Shu: Electrical Design Department, Zhenjiang Hongye Science & Technology Co., Ltd., Zhenjiang 212000, China

Energies, 2022, vol. 15, issue 4, 1-16

Abstract: This paper proposes a fault diagnosis method for ship electrical power systems on the basis of an improved convolutional neural network (CNN) to support normal ship operation. First, according to the mathematical model of the ship electrical power system, the simulation model of the ship electrical power system is built using the MATLAB/Simulink simulation software platform in order to understand the normal working state and fault state of the generator and load in the power system. Then, the model is simulated to generate the fault response curve, and the picture dataset of the network model is obtained. Second, a CNN fault diagnosis model is designed using TensorFlow, an open-source tool for deep learning. Finally, network model training is performed, and the optimal diagnosis results of the ship electrical power system are obtained to realize structural parameter optimization and diagnosis. The diagnosis results show that the established simulation model and improved CNN can provide support for fault diagnosis of the ship electrical power system, improve the operation stability and safety of the ship electrical power system, and ensure safety of the crew.

Keywords: fault diagnosis technology; improved convolutional neural network; ship electrical power system; Simulink; synchronous generator (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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