考虑时延特性优化的燃煤锅炉主蒸汽温度预测模型  

Prediction model of main steam temperature of coal-fired boiler considering time delay characteristic optimization

作　　者：杨春来 袁晓磊 殷喆 金飞 李剑锋 吴斌 刘学来 YANG Chunlai;YUAN Xiaolei;YIN Zhe;JIN Fei;LI Jianfeng;WU Bin;LIU Xuelai(State Grid Hebei Energy Technology Service Co.,Ltd.,Shijiazhuang 050021,China;School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)

机构地区：[1]国网河北能源技术服务有限公司,河北石家庄050021 [2]华北电力大学控制与计算机工程学院,北京102206

出　　处：《热力发电》2022年第8期124-129,共6页Thermal Power Generation

基　　金：国网河北省电力有限公司科技项目(TSS2020-19)。

摘　　要：构建精确的锅炉主蒸汽温度预测模型有利于提高其控制品质,考虑锅炉运行参数的时延特性对主蒸汽温度的预测精度具有较大影响。使用长短时记忆(LSTM)神经网络算法构建模型预测锅炉主蒸汽温度变化趋势,并针对锅炉运行参数时延特性的问题,提出利用离散粒子群算法实现网络模型输入变量时滞的优化。最后,基于某1000 MW燃煤锅炉的历史数据,验证时延特性优化后的主蒸汽温度预测模型。预测结果表明,该模型预测均方根误差为0.47℃,较传统方法构建的LSTM神经网络模型预测误差降低6%,预测精度更高。Building an accurate prediction model of boiler main steam temperature is conducive to improvingits control quality.The time delay characteristics of boiler operation parameters have a great impact on the prediction accuracy of main steam temperature.Long-short term memory(LSTM)neural network algorithm is adopted to establish a model to predict the change trend of boiler main steam temperature,and aiming atsolving the problem of determining the time delay characteristics of boiler operation parameters,discrete particle swarm optimization algorithm is used to optimize the time delay of network model input variables.Moreover,based on the historical data of a 1000 MW coal-fired boiler,the main steam temperature prediction model optimized by time delay characteristics is verified.The results show that,the prediction error of the optimized LSTM network model considering the time delay characteristics is 0.47℃,namely 6% smaller than thatof the conventional LSTM model,indicating it has a higher prediction accuracy.

关 键 词：燃煤锅炉 主蒸汽温度 预测模型 LSTM神经网络 时延特性 

分 类 号：TK268[动力工程及工程热物理—动力机械及工程]