Simulation Study on Superheated Steam Temperature Control of Supercritical Boiler Unit Based on Elman Neural Network Inverse Models

2019 ◽  
Author(s):  
Miaomiao Zhang ◽  
Xiaoyong Zhang ◽  
Changge Xiao ◽  
Ze Song ◽  
Jin Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Simulation Study ◽  
Superheated Steam ◽  
Boiler Unit ◽  
Steam Temperature ◽  
Elman Neural Network ◽  
Superheated Steam Temperature ◽  
Inverse Models

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 Application of anti integral saturation cascade PID control algorithm in superheated steam temperature control

2021 ◽  
Vol 2087 (1) ◽  
pp. 012038
Author(s):  
Zhe Sun ◽  
Chunhui Liu ◽  
Ming Li ◽  
Junda Zhao ◽  
Yao Xu
Keyword(s):  
Temperature Control ◽  
Pid Control ◽  
Control Algorithm ◽  
Superheated Steam ◽  
Temperature Control System ◽  
Control Algorithms ◽  
Regulation System ◽  
Steam Temperature ◽  
Superheated Steam Temperature ◽  
Main Regulator

Abstract At present, most units adopt the superheated steam temperature control system with cascade PID control structure. However, because most cascade PID control algorithms have no anti-integral saturation function, the automatic control of boiler superheated steam temperature cannot be put into operation normally. On the existing cascade PID superheated steam temperature regulation system, we judge the deviation value of the main regulator and the output value of the auxiliary regulator, and cancel the integration function at an appropriate time. When the input deviation signal turns over, the regulating system can immediately enter the normal regulating state, so that the cascade PID superheated steam temperature regulating system can timely and accurately regulate the superheated steam temperature. The fuel cost is saved by about 155,600 yuan per year.

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