Thermal Process System Identification Using Particle Swarm Optimization

System identification adopting an open loop step response curve is a feasible way to obtain the mathematic model of the control object. Due to the satisfying performance in global optimization, evolution computing (EC) methods such as genetic algorithm have been applied to the open loop step response curve analysis and achieved effective results. In this paper, particle swarm optimization (PSO) algorithm which is considered as a new relative addition to the EC methods is introduced to solve the system identification problem for thermal process control objects. Typical forms of transfer functions for the thermal process are adopted, utilizing PSO algorithm to estimate the parameters, for the convenient application of which, a set of software is also developed. With these softwares, some characters of the experimental data are specified by the user. And then the initial values for the model parameters are deduced from these characters. Around these initial values, a smaller search space is determined, within which the PSO algorithm searches the optima for the model parameters. Thus the search efficiency can be improved remarkably. The software has been applied in some power plants, the results of which prove the effectiveness of the method