Neural network based superheater steam temperature control for a large-scale supercritical boiler unit

2011 ◽  
Author(s):  
Liangyu Ma ◽  
Kwang Y. Lee
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Large Scale ◽  
Boiler Unit ◽  
Steam Temperature

Review of Control Strategies Employing Neural Network for Main Steam Temperature Control in Thermal Power Plant

2014 ◽  
Vol 66 (2) ◽  
Author(s):  
N. A. Mazalan ◽  
A. A. Malek ◽  
Mazlan A. Wahid ◽  
M. Mailah
Keyword(s):  
Neural Network ◽  
Power Plant ◽  
Temperature Control ◽  
Pid Control ◽  
Thermal Power Plant ◽  
Thermal Power ◽  
Heat Rate ◽  
Steam Temperature ◽  
Multiple Variables ◽  
Main Steam

Main steam temperature control in thermal power plant has been a popular research subject for the past 10 years. The complexity of main steam temperature behavior which depends on multiple variables makes it one of the most challenging variables to control in thermal power plant. Furthermore, the successful control of main steam temperature ensures stable plant operation. Several studies found that excessive main steam temperature resulted overheating of boiler tubes and low main steam temperature reduce the plant heat rate and causes disturbance in other parameters. Most of the studies agrees that main steam temperature should be controlled within ±5 Deg C. Major factors that influenced the main steam temperature are load demand, main steam flow and combustion air flow. Most of the proposed solution embedded to the existing cascade PID control in order not to disturb the plant control too much. Neural network controls remains to be one of the most popular algorithm used to control main steam temperature to replace ever reliable but not so intelligent conventional PID control. Self-learning nature of neural network mean the load on the control engineer re-tuning work will be reduced. However the challenges remain for the researchers to prove that the algorithm can be practically implemented in industrial boiler control.

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.

Application of Active Disturbance Rejection in Main Steam Temperature Control of Thermal Power Plant

2014 ◽  
Vol 716-717 ◽  
pp. 1658-1661
Author(s):  
Dong Wang ◽  
Rui Bo Zhu
Keyword(s):  
Temperature Control ◽  
Disturbance Rejection ◽  
Power Plants ◽  
Large Scale ◽  
Thermal Power ◽  
Feedback Signal ◽  
Steam Temperature ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Main Steam

In large-scale thermal power plants, the control of boiler main steam temperature functions as an indispensable part to improve the economic efficiency and ensure the unit’s safety. Currently, the main steam system is always characterized as a dynamic system with big delay, large inertia and huge uncertainties. Considering the characteristics above, the Active Disturbance Rejection Control (ADRC) is designed and serves as a creative solution to the problem of main steam temperature control. In this paper, a guiding temperature signal is adopted as an auxiliary feedback signal, which constitutes the cascade control loop of ADRC-PI.

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