基于S-PMemetic算法的电站锅炉再热器系统建模

针对再热器这类热工对象普遍存在非线性、大迟延、时变性以及多变量等特点,采用文化基因算法框架下,粒子群(PSO)算法和模拟退火(SA)算法相结合的S-PM emetic算法对660 MW超超临界机组直流锅炉再热器系统的现场数据进行了多组数据并行辨识。通过该方法得到了超超临界机组再热器7个传递函数通道的系统模型。仿真结果表明,S-PM emetic算法平衡了PSO算法的全局搜索能力和SA算法的局部寻优能力,更适用于辨识复杂的热工控制对象进一步提高了多变量辨识效率和精确度。

Power Station Boiler Based on S-PMemetic Algorithm Reheater System Modeling

For the thermal objects such as reheaters,there are generally of nonlinear,large delay,time-varying and multivariable characteristics.Based on the field data of the 660 MW ultra-supercritical unit DC boiler reheater system,under the framework of cultural genetic algorithm,the S-PMemetic algorithm is adopted,which combines the particle swarm optimization(PSO) algorithm and the simulated annealing(SA) algorithm.and accuracy.The system model of seven transfer function channels of the ultra-supercritical unit reheater obtained by this method lays a foundation for the design of thermal power unit simulation and reheat steam temperature control system.S-PMemetic balances the global search ability of PSO and the local optimization ability of SA.It is more suitable for identifying complex thermal control objects and further improving the efficiency of multivariate identification.