Identification of Main Steam Temperature System Based on Improved Particle Swarm Optimization

doi:10.16182/j.issn1004731x.joss.20-0609

Abstract

Abstract: Establishing an accurate mathematical model of main steam temperature is the basis of improving the performance of control system. Aiming at the problems of early maturity and slow convergence in traditional particle swarm optimization (PSO) algorithm in model identification, an improved PSO algorithm with shrinkage factor is proposed. The algorithm improves the global optimization capability and convergence speed of the algorithm by adjusting the shrinkage factor. The on-site operating data of a 350 MW circulating fluidized bed (CFB) boiler in a power plant in Shanxi province are used in the identification of the main steam model parameters, and the improved PSO algorithm is used to optimize the model parameters of the main steam temperature system. The validity of the model is verified by actual data on-site, which lays the foundation for the optimization of main steam temperature control of CFB boilers.

Key words: main steam temperature, improved particle swarm algorithm, shrinkage factor, field data

CLC Number:

TP391.9

Cao Zhenqian, Yin Jiang, Zhang Jinhua. Identification of Main Steam Temperature System Based on Improved Particle Swarm Optimization[J]. Journal of System Simulation, 2021, 33(10): 2411-2419.

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