基于XGBoost建模及改进灰狼优化算法的再热汽温预测优化控制

为改善燃煤机组频繁变负荷过程中再热汽温的控制效果,提出一种基于机器学习的再热汽温预测优化控制方法。首先利用机组变负荷历史运行数据和XGBoost算法进行再热汽温特性建模,并采用随机搜索算法对模型参数进行优化以提高其预测精度。以最终的模型为基础,采用改进的灰狼优化算法(IGWO)对烟气侧再热挡板开度和蒸汽侧喷水减温阀指令进行实时寻优,实现再热汽温的预测优化控制。利用仿真机进行优化控制仿真试验。试验结果表明：采用智能预测优化控制方案可有效改善再热汽温控制效果,明显减少减温喷水用量,有助于提高机组的经济性。

Reheat Steam Temperature Predictive Optimization Control based on XGBoost Modeling and Improved Grey Wolf Algorithm

In order to improve the control effect of reheat steam temperature (RST) during the frequent load change of coal fired units,a RST predictive optimization control approach based on machine learning was proposed. Firstly,the RST prediction model was developed with the historical variable load operation data by using the eXtreme Gradient Boosting (XGBoost) algorithm,and the model parameters were optimized with the random search method to improve its prediction accuracy. Based on the final well trained model,an improved grey wolf optimizer (IGWO) was employed to realize predictive optimization control of RST by searching the real time optimal instructions of the flue gas side reheat baffle opening and the steam side water spray desuperheating valve. Optimization control simulation tests were carried out with a full scope simulator. The experimental results show that the intelligent predictive optimization control scheme proposed in this paper can effectively improve the control effect of RST,and significantly reduce the amount of desuperheating water spray,which helps to improve the economy of the unit.