Intelligent decision-making of start-up and shutdown for coal milling system in thermal power plants based on deep reinforcement learning

CAI Jiachen; LI Jun; GAO Ming; GAO Lin; GAO Yaokui; CHANG Peng

doi:10.19666/j.rlfd.202307118

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Intelligent decision-making of start-up and shutdown for coal milling system in thermal power plants based on deep reinforcement learning

更新时间：2025-09-28

- Intelligent decision-making of start-up and shutdown for coal milling system in thermal power plants based on deep reinforcement learning
- Thermal Power Generation Vol. 53, Issue 3, Pages: 146-152(2024)
- 作者机构：
  
  1. 西安热工研究院有限公司
  2. 陕西延长石油富县发电有限公司
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.202307118
  CLC： TP18;TM621
- Published：2024
- 稿件说明：
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CAI Jiachen, LI Jun, GAO Ming, et al. Intelligent decision-making of start-up and shutdown for coal milling system in thermal power plants based on deep reinforcement learning[J]. Thermal power generation, 2024, 53(3): 146-152.
DOI：

CAI Jiachen, LI Jun, GAO Ming, et al. Intelligent decision-making of start-up and shutdown for coal milling system in thermal power plants based on deep reinforcement learning[J]. Thermal power generation, 2024, 53(3): 146-152. DOI： 10.19666/j.rlfd.202307118.

摘要

针对目前人工决策基础上的制粉系统一键启停技术存在决策主观经验性强、操盘劳动强度大、节能优化潜力难以发掘等问题，提出了一种综合考虑制粉系统能耗与机组负荷跟踪性能的制粉系统启停决策评价模型，以安全引入网调负荷计划指令信号作为输入，研究了基于深度强化学习的制粉系统自启停智能决策方法，开发了制粉系统自启停决策闭环控制系统。研究结果通过仿真验证，并已在某超超临界1 000 MW机组常用磨煤机上成功应用，节能降耗效果显著。研究结果可为火电机组少人、无人化运行技术提供有效借鉴。

Abstract

A comprehensive evaluation model for the start-up and shutdown decision-making of the milling system

taking into account the energy consumption and tracking performance of the unit load

has been proposed to address issues such as subjective decision-making based on manual experience

high labor intensity in operation

and difficulty in exploring energy-saving optimization potential. This model safely incorporates the grid load scheduling command signal as input. Furthermore

a milling system start-stop intelligent decision-making method based on deep reinforcement learning has been studied

and a closed-loop control system for the automatic start-stop of the milling system has been developed. The research results have been verified through simulation and successfully applied to a commonly used coal milling system in a certain ultra-supercritical 1 000 MW unit

achieving energy savings. The findings of this study can provide effective reference for the development of unmanned or minimally manned operation techniques for thermal power units.

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Control strategy optimization for supercritical unit equipped with double-inlet and double-outlet ball mill

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