Research on DCS-SIS Dynamic Collaborative Control Strategy of Thermal Power Plant Centralized Control System Based on Deep Learning

YAO Lan

doi:10.13655/j.cnki.ibci.2025.S2.167

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Research on DCS-SIS Dynamic Collaborative Control Strategy of Thermal Power Plant Centralized Control System Based on Deep Learning

更新时间：2026-01-13

- Research on DCS-SIS Dynamic Collaborative Control Strategy of Thermal Power Plant Centralized Control System Based on Deep Learning
- 智能建筑与智慧城市 Issue S2, Pages: 532-534(2025)
- 作者机构：
  
  马鞍山当涂发电有限公司
- 作者简介：
- 基金信息：
- DOI：10.13655/j.cnki.ibci.2025.S2.167
  CLC： TM621;TP18
- Published：30 December 2025
- 稿件说明：
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YAO Lan. Research on DCS-SIS Dynamic Collaborative Control Strategy of Thermal Power Plant Centralized Control System Based on Deep Learning[J]. 智能建筑与智慧城市, 2025, (S2): 532-534.
DOI：

YAO Lan. Research on DCS-SIS Dynamic Collaborative Control Strategy of Thermal Power Plant Centralized Control System Based on Deep Learning[J]. 智能建筑与智慧城市, 2025, (S2): 532-534. DOI： 10.13655/j.cnki.ibci.2025.S2.167.

摘要

火电厂集控系统对控制策略的动态响应性提出更高要求，文章研究了DCS与SIS系统的功能结构与协同控制机制，分析了基于深度学习的状态识别方法与策略优化路径，最后探讨了控制策略在典型工况下的性能评估结果。研究成果可为复杂工况下集控系统的自动化运行提供数据驱动的控制支撑，提升安全性与响应效率。

Abstract

The centralized control system of thermal power plants puts forward higher requirements for the dynamic responsiveness of control strategies. The article studies the functional structure and collaborative control mechanism of DCS and SIS systems

analyzes the state identification method and strategy optimization path based on deep learning

and finally discusses the performance evaluation results of control strategies under typical working conditions. The research results can provide data-driven control support for the automated operation of centralized control systems under complex working conditions

and improve security and response efficiency.

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references

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Related Institution

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