Study on Heat Transfer Coefficient of High-pressure Heater based on DA-DBSCAN
In order to detect and deal with the operating economic abnormality of high pressure heater in time,this study used heat transfer coefficient to directly reflect the operating efficiency of high pressure heater,and put forward an online dynamic model of heat transfer coefficient based on time series data a-nalysis method.First,the main characteristic parameters affecting the heat transfer coefficient of the high-pressure heater were obtained through thermodynamic mechanical analysis,and a dynamic model based on the characteristic parameters was established;second,an optimal clustering model with optimal neighborhood parameters was constructed by the density-based spatial clustering of applications with noise(DBSCAN)method improved by the dragonfly algorithm(DA)to obtain the credible end-difference in-terval.Through a period of comparative calculation results of a certain power plant,it is shown that the mean square error(MSE)of the heat transfer coefficient of the high-pressure heater is as low as 0.030 5％based on the online dynamic model of the heat transfer coefficient of the DA-DBSCAN,indi-cating that the model is effective and feasible.
high-pressure heaterheat transfer coefficientdata processingdragonfly algorithm(DA)density-based spatial clustering of applications with noise(DBSCAN)
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