Research on Construction Method of Neural Network Model for Small Gas Turbine Under Variable Working Conditions Based on Mechanism Modification

YE Yiyin; HAN Wei; LI Jichao; MA Wenjing; HAN Zepeng; SONG Xinyang

doi:10.19805/j.cnki.jcspe.2025.240030

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Research on Construction Method of Neural Network Model for Small Gas Turbine Under Variable Working Conditions Based on Mechanism Modification

更新时间：2026-01-27

- Research on Construction Method of Neural Network Model for Small Gas Turbine Under Variable Working Conditions Based on Mechanism Modification
- Chinese Journal of Power Engineering Vol. 45, Issue 4, Pages: 505-513(2025)
- 作者机构：
  
  1. 中国科学院大学,北京,100190
  2. 中国科学院工程热物理研究所,北京,100190
  3. 华北电力大学能源动力与机械工程学院,北京,102206
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240030
  CLC： TK471;TP183
- Published Online：28 April 2025，
  
  Published：2025
- 稿件说明：
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叶旖茵,韩巍,李济超,马文静,韩泽鹏,宋新阳. 基于机理修正的小型燃气轮机变工况神经网络模型构建方法研究动力工程学报, 2025, 45(4): 505-513 https://doi.

org/10.19805/j.cnki.jcspe.2025.240030
叶旖茵,韩巍,李济超,马文静,韩泽鹏,宋新阳. 基于机理修正的小型燃气轮机变工况神经网络模型构建方法研究动力工程学报, 2025, 45(4): 505-513 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240030.

org/10.19805/j.cnki.jcspe.2025.240030 DOI：

摘要

燃气轮机变工况热力性能具有较强的非线性特点

基于物理模型的方法计算耗时长且计算误差较大

而基于数据驱动的方法对样本数据数量依赖强且计算过程中没有物理机理上的约束。为了克服这两种方法存在的问题

根据小型燃气轮机压缩和膨胀过程的物理机理

提炼出变工况运行的内外部影响参数与发电效率、排烟温度及排烟流量之间存在的约束关系

并结合神经网络基本框架

提出了基于机理修正的小型燃气轮机变工况神经网络模型(即改进模型)。具体算例的结果表明:利用已知的燃气轮机部件性能和部分数据样本

即可建立高精度的燃气轮机变工况神经网络模型;改进模型对关键参数的预测相对误差在0.941%以下

预测平均相对误差为0.583%

其预测平均相对误差为使用相同样本的基于纯数据驱动的神经网络预测误差的70.122%

为基于物理模型的方法计算误差的14.916%。

Abstract

The thermal performance of gas turbines under variable working conditions exhibits strong non-linear characteristics. Calculations based on physical models are time-consuming and have larger calculation errors

while data-driven methods depend heavily on the quantity of sample data and lack constraints from physical mechanisms during the calculation process. To overcome the shortcomings of both approaches

the constraints among the internal and external influencing parameters

power generation efficiency

exhaust temperature and exhaust flow rate under variable working conditions were extracted from the physical mechanism of the compression and expansion processes of small gas turbines. Combined with the basic framework of neural network

a neural network model for small gas turbines operated under variable working conditions was proposed based on mechanism modification. The results of specific calculations indicate that high-precision neural network model of gas turbines under variable working conditions can be established using known gas turbine component performance and some data samples. The relative error is less than 0.941% in the prediction of key parameters by the improved model. The average relative error of prediction is 0.583%

which is 70.122% of the prediction error of the purely data-driven neural network using the same samples

and 14.916% of the calculation error of the method based on the physical model.

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references

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