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Fault Diagnosis Technology for Ship Electrical Power System

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chaochun Yu & Liang Qi & Jie Sun & Chunhui Jiang & Jun Su & Wentao Shu, 2022. "Fault Diagnosis Technology for Ship Electrical Power System," Energies, MDPI, vol. 15(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1287-:d:746138
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    References listed on IDEAS

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    1. Hong-Keun Ji & Guoming Wang & Gyung-Suk Kil, 2020. "Optimal Detection and Identification of DC Series Arc in Power Distribution System on Shipboards," Energies, MDPI, vol. 13(22), pages 1-16, November.
    2. Monaaf D. A. Al-Falahi & Tomasz Tarasiuk & Shantha Gamini Jayasinghe & Zheming Jin & Hossein Enshaei & Josep M. Guerrero, 2018. "AC Ship Microgrids: Control and Power Management Optimization," Energies, MDPI, vol. 11(6), pages 1-20, June.
    3. Haris E. Psillakis & Antonio T. Alexandridis, 2020. "Coordinated Excitation and Static Var Compensator Control with Delayed Feedback Measurements in SGIB Power Systems," Energies, MDPI, vol. 13(9), pages 1-18, May.
    4. Aleksandar Cuculić & Dubravko Vučetić & Rene Prenc & Jasmin Ćelić, 2019. "Analysis of Energy Storage Implementation on Dynamically Positioned Vessels," Energies, MDPI, vol. 12(3), pages 1-19, January.
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    Cited by:

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