基于遗传算法的600MW单元机组建模

Genetic Algorithm Based 600MW Plant Unit Modeling

研究火电发电机组锅炉控制建模,大型单元机组的控制对象具有强耦合、非线性、不确定性等特点,传统的建模方法很难得到最佳机组模型。为了将机理建模与实验建模进行结合,采用机理建模与实验建模相结合的方法,在对某台600MW级、采用亚临界压力控制循环汽包锅炉、一次中间再热、单轴、三缸四排汽、直接空冷凝汽式汽轮机单元机组各项特性进行分析的基础上,经过假设和简化得出机组的机理模型。再采用改进的遗传算法对现场数据的辨识结果进行优化得出单元机组的非线性模型。对非线性模型进行线性化,得到不同负荷-压力工作点下对象的线性模型。对模型进行扰动仿真。仿真结果表明建模方法有效。

Large unit plant control object has the characteristics of strong coupling, nonlinear and uncertainty, so it is difficult to use the traditional modeling method to get the best unit model. The difficulty of modeling is to com- bine the mechanism modeling with experiment modeling. Based on the method of combining mechanism modeling with experimental modeling and the analysis on various characteristics such as a drum boiler of 600 mw grade with subcritical pressure controlled circulation, a turbine unit set with a reheat, single axis, three cylinder four exhaust steam and direct air condensing steam, after the hypothesis and simplified, we drew out the unit mechanism model expression, and then used an improved genetic algorithm to optimize unit plant nonlinear model which was from the field data identification results. The nonlinear model was transformed into linear model to get the object linear model under different load - pressure working point. A disturbance simulation experiment was proceeded. The simulation results show that the modeling method is effective.