基于模糊智能预测控制的燃煤机组低温省煤器多变量解耦控制策略

张明琦; 李军; 高林; 王林; 周俊波; 高耀岿; 雷杨祥; 王大伟; 王立成

doi:10.19805/j.cnki.jcspe.2025.250375

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基于模糊智能预测控制的燃煤机组低温省煤器多变量解耦控制策略

更新时间：2026-10-10

- 基于模糊智能预测控制的燃煤机组低温省煤器多变量解耦控制策略
- A Multivariable Decoupling Control Strategy for a Low-temperature Economizer in a Coal-fired Power Unit Based on Fuzzy Intelligent Predictive Control
- 动力工程学报 2025年45卷第12期页码：2034-2043
- 作者机构：
  
  1. 西安热工研究院有限公司,陕西,西安,710054
  2. 陕西延长石油富县发电有限公司,陕西,延安,727599
- 作者简介：
  
  张明琦(2001—),男,陕西西安人,硕士研究生,研究方向为智能发电控制技术,E-mail:1196137309@qq.com
- 基金信息：
  
  华能集团科技资助项目(HNKJ24-H136)
- DOI：10.19805/j.cnki.jcspe.2025.250375
  中图分类号：
- 网络首发：2025-12-16，
  
  纸质出版：2025-12-16
- 稿件说明：
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张明琦,李军,高林,王林,周俊波,高耀岿,雷杨祥,王大伟,王立成. 基于模糊智能预测控制的燃煤机组低温省煤器多变量解耦控制策略动力工程学报, 2025, 45(12): 2034-2043 https://doi.

org/10.19805/j.cnki.jcspe.2025.250375
张明琦,李军,高林,王林,周俊波,高耀岿,雷杨祥,王大伟,王立成. 基于模糊智能预测控制的燃煤机组低温省煤器多变量解耦控制策略动力工程学报, 2025, 45(12): 2034-2043 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.250375.

org/10.19805/j.cnki.jcspe.2025.250375 DOI：

摘要

针对燃煤机组低温省煤器系统的大迟延、大惯性、强耦合等复杂的运行特性

提出了一种基于模糊智能预测控制的多变量解耦控制策略。首先

建立以省煤器出口烟温和暖风器出口风温为被控量、水泵变频器和暖风器出口调节阀为控制量的多变量耦合数学模型

并利用金豺算法优化的偏最小二乘法辨识获得模型参数。在此基础上

基于模糊智能预测控制算法设计多变量前馈解耦策略

对低温省煤器系统进行解耦控制。仿真结果表明:相比于传统控制方法

基于模糊智能预测算法的多变量解耦控制具有更好的控制精度和鲁棒性。实际应用结果表明:省煤器出口烟温和暖风器出口风温采用模糊智能预测算法的多变量解耦控制策略控制效果良好

其中实际出口烟温与设定值偏差小于5 ℃

实际出口风温与设定值偏差小于3 ℃

提高了系统的稳定性

并且波动幅度明显减小

符合工业生产要求。

Abstract

To address the complex operational characteristics of the low-temperature economizer system of coal-fired units

such as large time delay

significant inertia and strong coupling

a multivariate decoupling control strategy based on fuzzy intelligent predictive control was proposed. First

a multivariate coupled mathematical model was established

taking the flue gas temperature at the outlet of the economizer and the air temperature at the outlet of the heater as the controlled variables

and the water pump frequency converter and the air heater outlet control valve as the control variables. The model parameters were then identified using the partial least squares method optimized by the golden jackal optimization algorithm. Building on this model

a multivariate feed forward decoupling strategy was designed based on the fuzzy intelligent predictive control algorithm to decouple the control for the low-temperature economizer system. The simulation results show that compared with the traditional control method

the multivariate decoupling control based on the fuzzy intelligent predictive algorithm exhibits superior control accuracy and robustness. The practical application results indicate that the multivariate decoupling control strategy using fuzzy intelligent predictive algorithm achieves excellent control performance for both the economizer outlet flue gas temperature and the air heater outlet air temperature. The deviation between the actual outlet flue gas temperature and its setpoint is kept within 5 ℃

and the deviation between the actual outlet air temperature and its setpoint is kept within 3 ℃. This has enhanced the stability of the system and significantly reduced fluctuation amplitude

meeting the requirements of industrial production.

关键词

Keywords

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