The simulation research and neural network modeling of superheated steam temperature characteristics for ultra-supercritical unit

2015 ◽  
Author(s):  
Yongguang Ma ◽  
Shiru Yang ◽  
Shuo Cai ◽  
Yufang Wang
Keyword(s):  
Neural Network ◽  
Superheated Steam ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Steam Temperature ◽  
Simulation Research ◽  
Temperature Characteristics ◽  
Superheated Steam Temperature

Superheated Steam Temperature Control Based on Improved Recurrent Neural Network and Simplified PSO Algorithm

2011 ◽  
Vol 128-129 ◽  
pp. 1065-1069 ◽  
Author(s):  
Liang Yu Ma ◽  
Yin Ping Ge ◽  
Xing Cao
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Recurrent Neural Network ◽  
Power Plants ◽  
Superheated Steam ◽  
Steam Temperature ◽  
Superheated Steam Temperature ◽  
Control Scheme ◽  
Wide Range ◽  
Optimization Control

Coal-fired power plants are facing a rapid developing tide toward supercritical and ultra-supercritical boiler units with higher parameters and bigger capacity. Due to the large inertia, large time delay and nonlinear characteristics of a boiler’s superheater system, the widely-used conventional cascade PID control scheme is often difficult to obtain satisfactory steam temperature control effect under wide-range operating condition. In this paper, a predictive optimization control method based on improved mixed-structure recurrent neural network model and a simpler Particle Swarm Optimization (sPSO) algorithm is presented for superheated steam temperature control. Control simulation tests on the full-scope simulator of a 600 MW supercritical power unit showed that the proposed predictive optimization control scheme can greatly improve the superheated steam temperature control quality with good application prospect.

Improved CMAC Neural Network Control for Superheated Steam Temperature

2011 ◽  
Vol 383-390 ◽  
pp. 111-117 ◽  
Author(s):  
Li Jun Chen ◽  
Bo Sun ◽  
Jian Chao Diao ◽  
Li Li Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Superheated Steam ◽  
Network Control ◽  
Temperature Control System ◽  
Neural Network Control ◽  
Steam Temperature ◽  
Cmac Neural Network ◽  
Superheated Steam Temperature

Aiming at that superheated steam temperature system exists the large inertia and large time delay of the dynamic characteristics,and the converge speed of the conventional CMAC neural network is not fast enough to the real-time system, a credit assignment CMAC (CA-CMAC) neural network control is adopted in superheated steam temperature control system, which is proposed to speed up the learning process in CMAC. The simulation of the superheated steam temperature control system shows that CA-CMAC converges faster than the conventional CMAC. This result illustrates the effectiveness of this method.

scholarly journals Superheated steam temperature system of thermal power control engineering based on neural network local multi-model prediction

2021 ◽  
Vol 25 (4 Part B) ◽  
pp. 2949-2956
Author(s):  
Jinsong Zhan ◽  
Jing Du ◽  
Shaofeng Dong ◽  
Wei Hu
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Superheated Steam ◽  
Thermal Power ◽  
Time Lag ◽  
Operating Conditions ◽  
Steam Temperature ◽  
Superheated Steam Temperature ◽  
Smooth Switching ◽  
Switching Method

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.

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