Research On The Self-correction Of Thermal Equipment Simulation Model Based On The Genetic Algorithm

As more and more high parameter units with large capacity are put into operation, the simulation training is playing more and more important role in training operating personnel. At present, in the simulation system development, the simulation models require the developers to adjust the model coefficient continuously based on the service data of reference unit or thermodynamic calculation in order to satisfy the required precision, which causes to work-load and low modeling efficiency. And the modeling has bad reusability with no changeability as the simulate unit or thermal equipment change the thermodynamic property for a variety of causes. For the above questions, this paper made some improvements combined with the subject of the study after researching on the basic theory of the genetic algorithm and the implementation of this issue; the shell and tube steam heater of units with large capacity was divided into steam district, air district and drain district in accordance with property of different medium, then the paper established the heater dynamic simulation model with energy and quality conservation by STAR-90. Based the above research, the paper presents a standpoint on the self-correction of thermal equipment model with the genetic algorithm and put it into 600MW simulation system of HEBI power plant. Through the error analysis and simulation testing, it indicates that the self-correction simulation model of the heater can not only adjust parameter automatically according to the actual operating condition, but also increase the precision and the reusability of simulation models. The study of this paper may change the process of modeling debug from the manual operating method largely relying on the modeling personnel's experience to auto completion by computer program. Even, it can reduce the workload and difficulty in developing the simulation system and improve the modeling efficiency of research staff as much as possible. It provides a new way for parameter self-correction for thermal device mechanism models in the power plant.