Data-driven Coordinated Control for a 600 MW Coal-Ammonia Co-firing Unit

HAO Yafeng; CUI Liming; SHI Liang; LI Chi; FU Jing; BAO Lun; WANG Yinsong

doi:10.19805/j.cnki.jcspe.2026.250214

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Data-driven Coordinated Control for a 600 MW Coal-Ammonia Co-firing Unit

更新时间：2026-07-16

- Data-driven Coordinated Control for a 600 MW Coal-Ammonia Co-firing Unit
- Chinese Journal of Power Engineering Vol. 46, Issue 7, Pages: 176-184(2026)
- 作者机构：
  
  1. 华北电力大学自动化系,河北,保定,071003
  2. 神华集团有限责任公司,北京,100011
  3. 国能粤电台山发电有限公司,广东,江门,529228
  4. 烟台龙源电力技术股份有限公司,山东,烟台,264006
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250214
  CLC： TM621;TM273
- Published：2026
- 稿件说明：
移动端阅览
郝亚峰, 崔立明, 施梁, 李驰, 付静, 包伦, 王印松. 基于数据驱动的600 MW煤氨混合机组协调控制[J]. 动力工程学报, 2026, 46(7): 176-184.
郝亚峰, 崔立明, 施梁, 李驰, 付静, 包伦, 王印松. 基于数据驱动的600 MW煤氨混合机组协调控制[J]. 动力工程学报, 2026, 46(7): 176-184. DOI： 10.19805/j.cnki.jcspe.2026.250214.

DOI： 10.19805/j.cnki.jcspe.2026.250214

摘要

针对混氨燃烧协调控制系统中被控对象模型不确定性增加导致的主蒸汽压力、主蒸汽温度波动剧烈等问题

提出了一种基于数据驱动的自适应积分滑模控制方法(DAISMC)。首先

基于粒子群优化(PSO)算法构建能直观表达混氨特性的协调控制系统被控对象的详细模型;其次

利用离散时间预测技术实现系统原始模型中时滞项的隐形表达

同时

结合无模型自适应控制(MFAC)与离散滑模控制(ISMC)设计考虑扰动项的数据驱动控制器;最后

利用基于现场实际数据构建的传递函数模型和神经网络模型进行仿真验证。结果表明:所提控制方法对模型依赖性极弱

且具有较高的仿真精度和较强的鲁棒性。

Abstract

To address the issues of severe fluctuations in main steam pressure and temperature caused by increased model uncertainties of the controlled object in the ammonia co-firing coordinated control system

a data-driven adaptive integral sliding mode control (DAISMC) method was proposed. First

based on the particle swarm optimization (PSO) algorithm

a detailed model of the controlled object in the coordinated control system was established to explicitly characterize the ammonia co-firing characteristics. Second

discrete-time prediction technology was utilized to implicitly express the time-delay terms in the original system model. Simultaneously

a data-driven controller considering disturbance terms was designed by combining model-free adaptive control (MFAC) with discrete sliding mode control (DSMC). Finally

using a transfer function model and a neural network model constructed from actual field data. Results show that the proposed control method has minimal model dependence

along with high simulation accuracy and strong robustness.

关键词

Keywords

references

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