基于混合深度学习的燃气轮机动态过程关键参数在线辨识  

Hybrid deep learning-based online identification method for key parameters of gas turbine dynamic process

作　　者：孙守泰 薛亚丽[2] 王明春[1] 孙立 SUN Shoutai;XUE Yali;WANG Mingchun;SUN Li(Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education,School of Energy and Environment,Southeast University,Nanjing 210018,China;State Key Laboratory of Electric Power Systems,Department of Energy and Power Engineering,Tsinghua University,Beijing 100084,China)

机构地区：[1]东南大学能源与环境学院能源热转换与控制教育部重点实验室,江苏南京210018 [2]清华大学能源与动力工程系电力系统国家重点实验室,北京100084

出　　处：《中国舰船研究》2023年第3期222-230,共9页Chinese Journal of Ship Research

基　　金：国家科技重大专项资助项目(2017-I-0002-0002)。

摘　　要：[目的]为克服燃气轮机非线性时变特性对动态控制及性能监测的影响,通过长短期记忆神经网络(LSTM)的时序记忆、非线性关系表达与高斯过程回归(GPR)的区间概率估计能力三者的结合,提出一种基于LSTM-GPR混合深度学习模型的关键动态参数在线辨识算法。[方法]首先,建立燃气轮机的动态机理模型,以燃料热值、压气机效率及负载电力矩为待辨识参数,生成大量训练数据;然后,构建LSTM-GPR参数辨识网络模型,并输入训练数据进行网络训练和权重系数学习;最后,使用训练好的LSTM-GPR混合模型对燃气轮机动态运行参数进行在线辨识,经分析辨识结果来验证所提算法的有效性。[结果]仿真结果表明,所提算法辨识结果准确,误差小于1%,实时性好,相比于LSTM单一模型能获得更好的均值估计效果,并给出可靠的结果置信区间。[结论]所提算法能有效应用于燃气轮机模型的关键动态参数在线辨识,为进一步应用于实际机组奠定了基础。[Objective]In order to overcome the influence of the nonlinear time-varying characteristics of gas turbines on dynamic control and performance monitoring,this paper combines the time series memory and nonlinear relation expression ability of a long short-term memory neural network(LSTM)with the interval probability estimation ability of Gaussian process regression(GPR)to propose an online parameter identifica-tion algorithm for the key dynamic parameters of gas turbines based on an LSTM and GPR-based hybrid deep learning model(LSTM-GPR).[Methods]First,the dynamic mechanism model of a gas turbine is estab-lished,and a large amount of training data is generated by taking fuel calorific value,compressor efficiency and load power moment as the parameters to be identified.Next,the parameter identification network model of LSTM-GPR is constructed,and the training data is input for network training and weight coefficient learning.Finally,the trained LSTM-GPR hybrid deep learning model is used to identify the dynamic operat-ing parameters of the gas turbine model online,and the identification results are analyzed to verify the effect-iveness of the proposed algorithm.[Results]The simulation results show that the online identification res-ults of the proposed LSTM-GPR hybrid model algorithm are accurate,with a recognition error of less than 1%and good real-time performance.Compared with the LSTM single model,the proposed algorithm can obtain a better mean estimation effect and provide a reliable confidence interval range.[Conclusions]The LSTM-GPR hybrid algorithm can be effectively applied to the online parameter identification of a gas turbine model,laying a foundation for its further application to the dynamic operation parameter identification of practical units.

关 键 词：燃气轮机模型 在线参数辨识 长短期记忆神经网络 高斯过程回归 

分 类 号：U664.13[交通运输工程—船舶及航道工程]