The flow characteristic of steam turbine is one of the important characteristics of main control system for thermal power units

which affects the safety

energy conservation and stable running of the units. In this paper

the flow characteristic model of steam turbine is built for simulation and optimization model

and the improved quantum particle swarm optimization(QPSO) algorithm is put forward with double-target simulation optimization function

to solve the coupling problem between flow characteristic linearity and valves overlap

so as to realize optimization for flow characteristic of the steam turbine. Moreover

the simulation optimization results are applied to the actual production to overcome the difficulties of flow characteristic mutations

poor linearity

and large overlap degree. Through analyzing the main steam pressure loss and heat rate

the energy conservation situation of the steam turbine unit before and after the optimization is compared. The results show that

this method can improve the steam quality effectively

reduce the steam throttling loss and contribute to the energy conservation operation level of the steam turbine unit.