基于改进PSO算法的火电厂锅炉主汽温控制研究OA

To address the issues of poor control stability in existing main steam temperature control algorithms,a control scheme based on improved particle swarm optimization(PSO)was proposed.Through boiler steam flow disturbance analysis,the core control variables of main steam temperature were determined,and an improved PSO algorithm model was constructed.A dynamic nonlinear parameter assignment strategy was introduced,where the inertia weight and learning factors were set as variable values,and optimal solutions were selected based on iteration counts.Meanwhile,a proportional integral derivative(PID)controller was integrated to enhance the coordinated control effect of primary and secondary parameters,achieving stable main steam temperature control.Simulation experiments were conducted using the BoilerSim simulation software and compared with other control algorithms.The results indicate that the proposed scheme achieves a main steam temperature variance of 0.125 and a secondary desuperheater water supply flow variance of 0.223,both of which are lower than those of the classical PSO control algorithm(main steam temperature variance:0.557,secondary desuperheater water supply flow variance:0.882),fuzzy adaptive PID control algorithm(main steam temperature variance:0.265,secondary desuperheater water supply flow variance:1.125),and neural network PID control algorithm(main steam temperature variance:0.271,secondary desuperheater water supply flow variance:1.131).The control stability is significantly improved.This control method demonstrates excellent control performance by improving control accuracy and reducing deviation.It shows good applicability,providing reliable technical support for practical engineering applications.