Modeling and Operation Optimization of Steam Turbine Cold End System Based on Data Driven

CHENG Bo; LU Xuxiang; LIU Yufei; LONG Quanli; ZHANG Zhibin

doi:10.19805/j.cnki.jcspe.2025.230774

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Modeling and Operation Optimization of Steam Turbine Cold End System Based on Data Driven

更新时间：2026-02-02

- Modeling and Operation Optimization of Steam Turbine Cold End System Based on Data Driven
- Chinese Journal of Power Engineering Vol. 45, Issue 3, Pages: 359-365(2025)
- 作者机构：
  
  1. 长沙理工大学能源与动力工程学院,湖南,长沙,410114
  2. 长安益阳发电有限公司,湖南,益阳,413000
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.230774
  CLC： TM621;TK261
- Published Online：15 March 2025，
  
  Published：2025
- 稿件说明：
移动端阅览
程波,卢绪祥,刘雨菲,龙权利,张志斌. 基于数据驱动的汽轮机冷端系统建模与运行优化动力工程学报, 2025, 45(3): 359-365 https://doi.

org/10.19805/j.cnki.jcspe.2025.230774
程波,卢绪祥,刘雨菲,龙权利,张志斌. 基于数据驱动的汽轮机冷端系统建模与运行优化动力工程学报, 2025, 45(3): 359-365 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.230774.

org/10.19805/j.cnki.jcspe.2025.230774 DOI：

摘要

为了提高汽轮机冷端系统的节能效果

提出了一种数据驱动的冷端系统建模与运行优化方法。首先对获取到的机组历史运行数据进行稳态筛选

然后结合机理分析和机器学习算法挑选特征建立汽轮机的发电功率预测模型及凝汽器的压力预测模型

最后改变冷端设备的运行方式并将其带入模型进行寻优。同时将其应用到某630 MW机组中进行实际预测和模型验证。结果表明:所建立的预测模型具有较好的预测精度

能够实时反映机组的真实运行情况并为冷端系统的运行优化提供参考

有助于进一步推进火电机组的节能减排与智慧化运行。

Abstract

To improve energy-saving effect of steam turbine cold end system

a data-driven modeling and operation optimization method for a steam turbine cold end system was proposed. Firstly

steady-state screening was performed on the obtained historical operating data of the turbine. Then

combining mechanism analysis and machine learning algorithms to select features

a power generation prediction model for the steam turbine and a pressure prediction model of its condenser were established. Finally

the operating mode of the cold end equipment was changed and incorporated into the model for optimization. It was applied to a 630 MW unit for actual prediction and model validation. Results show that the established prediction model has good prediction accuracy

can reflect the real operating situation of the turbine in real time

and provide reference for the optimization of the cold end system operation

which is helpful to further promote the energy-saving

emission reduction

and intelligent operation of thermal power units.

关键词

Keywords

references

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