Modeling of Coal Mill System Used for Fault Simulation

Yong Hu, Boyu Ping, Deliang Zeng, Yuguang Niu and Yaokui Gao
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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

Energies, 2020, vol. 13, issue 7, 1-14

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.

Keywords: coal mill; dynamic model; data-driven; fault diagnosis; fault simulation (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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