TY - JOUR
TI - Superheated steam temperature system of thermal power control engineering based on neural network local multi-model prediction
AU - Zhan Jinsong
AU - Du Jing
AU - Dong Shaofeng
AU - Hu Wei
JN - Thermal Science
PY - 2021
VL - 25
IS - 4
SP - 2949
EP - 2956
PT - Article
AB - The superheated steam temperature object of thermal power plant has the  characteristics of time lag, inertia and time-varying parameters. The  control quality of the conventional proportional integral derivate  controller with fixed parameters will decrease after the object  characteristics change. The generalized predictive control strategy of  superheated steam temperature based on neural network local multi-model  switching can achieve the goal of designing sub-controllers for fixed models  under several typical operating conditions. When the system operating  conditions change, the effective switching strategy is timely and accurate.  Switch to the most suitable controller. The paper proposes a new smooth  switching method, which can effectively suppress the large disturbance  phenomenon of the object when switching. The simulation results verify the  effectiveness of the control strategy.