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Modeling of Coal Mill System Used for Fault Simulation

Author

Listed:
  • Yong Hu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Control and Computer Engineering College, North China Electric Power University, Beijing 102206, China)

  • Boyu Ping

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Control and Computer Engineering College, North China Electric Power University, Beijing 102206, China)

  • Deliang Zeng

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Control and Computer Engineering College, North China Electric Power University, Beijing 102206, China)

  • Yuguang Niu

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Control and Computer Engineering College, North China Electric Power University, Beijing 102206, China)

  • Yaokui Gao

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Control and Computer Engineering College, North China Electric Power University, Beijing 102206, China)

Abstract

Monitoring and diagnosis of coal mill systems are critical to the security operation of power plants. The traditional data-driven fault diagnosis methods often result in low fault recognition rate or even misjudgment due to the imbalance between fault data samples and normal data samples. In order to obtain massive fault sample data effectively, based on the analysis of primary air system, grinding mechanism and energy conversion process, a dynamic model of the coal mill system which can be used for fault simulation is established. Then, according to the mechanism of various faults, three types of faults (i.e., coal interruption, coal blockage and coal self-ignition) are simulated through the modification of model parameters. The simulation shows that the dynamic characteristic of the model is consistent with the actual object, the relative error of each output variable is less than 2.53%, and the total average relative error of all outputs is about 1.2%. The model has enough accuracy and adaptability for fault simulation, and the problem of massive fault samples acquisition can be effectively solved by the proposed method.

Suggested Citation

  • Yong Hu & Boyu Ping & Deliang Zeng & Yuguang Niu & Yaokui Gao, 2020. "Modeling of Coal Mill System Used for Fault Simulation," Energies, MDPI, vol. 13(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1784-:d:342585
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    References listed on IDEAS

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    1. Zeng, De-Liang & Hu, Yong & Gao, Shan & Liu, Ji-Zhen, 2015. "Modelling and control of pulverizing system considering coal moisture," Energy, Elsevier, vol. 80(C), pages 55-63.
    2. Tamilselvan, Prasanna & Wang, Pingfeng, 2013. "Failure diagnosis using deep belief learning based health state classification," Reliability Engineering and System Safety, Elsevier, vol. 115(C), pages 124-135.
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    Cited by:

    1. Jakov Batelić & Karlo Griparić & Dario Matika, 2021. "Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets," Energies, MDPI, vol. 14(18), pages 1-14, September.

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