Superheated Steam Temperature Control Based on Improved Recurrent Neural Network and Simplified PSO Algorithm
Coal-fired power plants are facing a rapid developing tide toward supercritical and ultrasupercritical boiler units with higher parameters and bigger capacity. Due to the large inertia, large time delay and nonlinear characteristics of a boilers 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.
supercritical boiler superheated steam temperature recurrent neural network particle swarm optimization predictive optimization control
Ma Liangyu Ge Yinping Cao Xing
Department of Automation, North China Electric Power University, Baoding 071003,China Department of Automation, North China Electric Power University, Baoding 071003, China
国际会议
三亚
英文
1065-1069
2012-01-06（万方平台首次上网日期，不代表论文的发表时间）