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.
Highlights
The main task of a coal mill system is to provide qualified fuel for the pulverized coal boiler
The effective monitoring and diagnosis of a coal mill system is very important for the security operation of a coal-fired power plant
Coal feed flow; the outputs of the coal mill system model are inlet air temperature (Tin ), inlet air pressure of mill, inlet air flow, outlet air pressure of mill and outlet temperature of mixture (Tout ); intermediate variables of model are raw coal stored in mill (Mrc ), coal powder stored in mill (Mpc ), grinding current (Ib ), coal powder flow and coal powder moisture. αi (i = 1, 2, 3, 4)
Summary
The main task of a coal mill system is to provide qualified fuel for the pulverized coal boiler. In reference [11], a roll mill model used to improve mill control for a better unit load tracking capability was established, in which the model of coal storage, powder storage, coal powder flow and the outlet temperature of mixture were taken into account, but this model does not consider the characteristic in fault state and cannot be used for fault simulation. Based on the model proposed in reference [14], by considering the influence of raw coal moisture on the model accuracy, an improved nonlinear dynamic model of MPS mill was built in reference [16], by which the outlet coal powder flow was monitored and a new control strategy was designed to improve the control accuracy of the system’s output [17].
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