数据驱动下火电机组时延鉴别及主汽温度预测  

Data driven time delay identification and main steam temperature prediction in thermal power units

作　　者：桂宁[1] 华菁云 GUI Ning;HUA Jingyun(School of Computer Science and Engineering,Central South University,Changsha Hunan 410083,China;School of Information Science and Technology,Zhejiang Sci-Tech University,Hangzhou Zhejiang 310038,China)

机构地区：[1]中南大学计算机学院,长沙410083 [2]浙江理工大学信息学院,杭州310038

出　　处：《计算机应用》2020年第11期3400-3406,共7页journal of Computer Applications

基　　金：国家自然科学基金面上项目(N61772473)。

摘　　要：针对传统的火电机组主汽温度建模时在海量特征和长机组延迟下的特征及对应时延的有效选择困难的问题,提出一种综合考虑特征选择和时延选择的融合模型的建模方法。针对火电机组特征的高维性,通过结合相关性系数和梯度提升机的特征选择以筛选出与主汽温度高相关的特征。针对时延鉴别,设计基于相关度的时延计算(TD-CORT)算法用以估计各参数与预测目标主汽温度之间的时延大小,并为预测目标和计算复杂度实现了滑动窗口大小的自动匹配。最后,采用深度神经网络(DNN)与长短期记忆(LSTM)的融合模型实现对火电机组主汽温度的预测。在国内某1000 MW超超临界燃煤机组的部署结果表明,所提方法的预测平均绝对误差(MAE)值达到0.1016,该方法相较未考虑时延的神经网络在预测准确度上提升了57.42%。With massive features and long unit delays,it is very difficult to effectively select the most appropriate features and corresponding delays during the modeling of the main steam temperature of thermal power unit.Therefore,a modeling method of the fusion model jointly considering feature selection and delay selection was proposed.Aiming at the high dimensionality of the features of thermal power units,the features highly associated with the main steam temperature were selected through the correlation coefficients and the feature selection of gradient boosting machine.For the delay identification,the Temporal Correlation Coefficient-based Time Delay(TD-CORT)calculation algorithm was designed to estimate the time delay between each parameter and the predicted target main steam temperature.And the automatic matching of the sliding window size was realized for the prediction target and the calculation complexity.Finally,the fusion model of Deep Neural Network(DNN)and Long Short-Term Memory(LSTM)was used to predict the main steam temperature of the thermal power unit.The deployment results on a 1000 MW ultra-supercritical coal-fired unit in China show that the proposed method has the prediction Mean Absolute Error(MAE)value of 0.1016,and the prediction accuracy 57.42%higher than the neural network without considering the delay.

关 键 词：火电机组 时延计算 主汽温度 特征选择 深度学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]