Identification of LPV model for superheated steam temperature system using A-QPSO

Abstract

Boiler superheated steam temperature control is of great importance for the safety and economy of thermal power unit. This article presents a nonlinear model identification method for the superheated steam temperature system, based on the unit load as a time-varying parameter. The linear parameter varying (LPV) model, which has been established by using the unit historical data, is suitable for control studies, and it can reflect the global dynamic characteristics of the object. To solve the model parameters estimation in high-dimensional problem, the evolutionary acceleration factor is introduced to the quantum particle swarm optimization (A-QPSO) algorithm to optimize the model parameters. The validity of the modified algorithm A-QPSO is verified through optimizing several classical high-dimensional standard test functions. The A-QPSO improved the optimization efficiency, and optimized result is very close to the optimal value. Finally, the proposed method is applied to the identification of a power plant superheated steam temperature process, and the results show that the optimized superheated steam temperature model is of high accuracy and the proposed method is feasible.