Dynamic characteristic optimization for superheater system model based on evolutionary computation

Genetic algorithm is presented to optimize dynamic characteristic of superheater system model. The dynamic characteristic is decomposed into three indexes to construct target function. Genetic algorithm is used to optimize model parameters. Validated with transfer function of final superheater system, the model optimized by this method achieves the required accuracy when inlet steam temperature disturbs. The method replaces manual parameter regulation and shortens the optimization time. As a general optimization frame, it provides a novel method of dynamic characteristic optimization not only for superheater model but also for other thermal device model in power plant simulator.