Open AccessSuperheated steam temperature system of thermal power control engineering based on neural network local multi-model prediction
Author(s) -
Jing Zhan,
Junbao Du,
Shen Dong,
Wei Hu
Publication year - 2021
Publication title -
thermal science/thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci2104949z
Subject(s) - superheated steam , control theory (sociology) , inertia , artificial neural network , computer science , controller (irrigation) , temperature control , thermal power station , superheating , lag , power (physics) , thermal , control engineering , control (management) , engineering , artificial intelligence , computer network , physics , classical mechanics , quantum mechanics , agronomy , biology , condensed matter physics , waste management , meteorology
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.