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针对某1000 MW超超临界机组,建立了具有较高精度和良好动态性能、考虑机组回热循环特性的机组负荷及主汽压力神经网络预测模型.在此基础上,提出了一种协调系统综合智能预测优化控制方法.该方法利用负荷及主汽压力预测模型在机组变负荷过程中分别对除氧器水位调门开度、汽轮机调门开度及燃料量指令进行实时优化,改善协调控制效果.借助1 000 MW超超临界机组仿真机,进行了详细的协调优化控制仿真试验.结果表明:该方法可有效提高机组动态过程负荷的响应速度和调节精度,大大减小变负荷过程中主汽压力的控制偏差,具有较好的工程实用性.
For a 1000 MW ultra-supercritical pressure unit, a model of predicting the unit load and the main steam pressure neural network with high precision and good dynamic performance and taking into account the thermal cycling characteristics of the unit is established. On this basis, a coordinated system synthesis This method uses the load and main steam pressure prediction model to optimize the opening degree of the deaerator water level and the opening degree of the turbine and the fuel quantity command in real time to improve the coordination and control effect during the variable load of the unit. 1 000 MW ultra-supercritical unit simulator, the detailed simulation research on coordinated optimization and control was carried out.The results show that this method can effectively improve the response speed and regulation precision of the dynamic load of the unit, greatly reducing the main steam pressure during variable load Control deviation, with good engineering practicability.