Modeling of Nonlinear Industrial System at All Operating Conditions Based on State Tracking

Authors

Dong Ze, Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation (North China Electric Power University), Baoding 071003, China;
Erxin Yin, Hebei Engineering Research Center of Simulation & Optimized Control for Power Generation (North China Electric Power University), Baoding 071003, China;

Abstract

Abstract: From the prospective of industrial big data modeling, this paper presents a modeling method for nonlinear industrial system at all operating conditions based on state tracking. In view of large amount of historical data and the difficulty to screen the modeling data, a sliding window is designed to screen steady-state data. The fast calculation method for the standard deviation is deducted. The influence mechanism of unknown disturbance on the system is analyzed. The data segment, representing the system from dynamic state to stable state, is selected as the modeling data. A data-driven modeling algorithm, which can effectively eliminate the disturbance influence, is proposed. The model information contained in the process industry big data is adopted and the high order function is applied to fit the model parameters. A linear transfer function model with variable parameter based on the characteristic parameters is proposed. The effectiveness of the proposed method is verified by modeling an industrial process.

Recommended Citation

Ze, Dong and Yin, Erxin (2019) "Modeling of Nonlinear Industrial System at All Operating Conditions Based on State Tracking," Journal of System Simulation: Vol. 30: Iss. 3, Article 10.
DOI: 10.16182/j.issn1004731x.joss.201803010
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss3/10

First Page

846

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201803010

Last Page

856

CLC

TP391.9

Recommended Citation

Dong Ze, Yin Erxin. Modeling of Nonlinear Industrial System at All Operating Conditions Based on State Tracking[J]. Journal of System Simulation, 2018, 30(3): 846-856.

DOI

10.16182/j.issn1004731x.joss.201803010