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

Abstract:

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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.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

1065-1069

DOI:

10.4028/www.scientific.net/AMM.128-129.1065

Citation:

L. Y. Ma et al., "Superheated Steam Temperature Control Based on Improved Recurrent Neural Network and Simplified PSO Algorithm", Applied Mechanics and Materials, Vols. 128-129, pp. 1065-1069, 2012

Online since:

October 2011

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$35.00

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