Article(id=1221497399336424292, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202208176, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1661356800000, receivedDateStr=2022-08-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769157274326, onlineDateStr=2026-01-23, pubDate=1682352000000, pubDateStr=2023-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769157274326, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769157274326, creator=13701087609, updateTime=1769157274326, updator=13701087609, issue=Issue{id=1221497393514730153, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='4', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769157272938, creator=13701087609, updateTime=1769157397933, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221497917878223060, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221497917878223061, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=104, endPage=112, ext={EN=ArticleExt(id=1221497399596471157, articleId=1221497399336424292, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Multi-condition adaptive control of combined heat and power unit based on deep reinforcement learning, columnId=1221467202000175547, journalTitle=Thermal Power Generation, columnName=Thermal energy and science research, runingTitle=null, highlight=null, articleAbstract=

Combined heat and power (CHP) units are affected by the output fluctuation of large-scale operating conditions, resulting in poor overall control quality of power generation load-extraction steam flow-throttle pressure in the turbine-boiler coordinated control system. To solve this problem, a multi-condition adaptive control method based on multi-agent deep deterministic policy gradient (MA-DDPG) is proposed. Firstly, according to the nonlinear dynamic mechanism of the unit, multiple operating condition sub-models considering the changes of state parameters are established, and the optimal operating condition sub-model is obtained by the integral function switching mechanism. For the multi-loop complex control requirements of the coordinated control system, a multi-agent synchronous operation mechanism is proposed, and the reward function is designed with the coordinated control objectives of rapid response, stable heating and safe operation. Finally, by training the agent to interact with the environment, the multi-loop gains are continuously adjusted online to achieve multi-condition adaptive control. Simulation results show that, compared with the conventional control method, the proposed method can effectively improve the load response rate under wide range operating conditions, and ensure the stability of heating.

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针对热电联产机组受大范围出力工况运行波动性影响，导致汽轮机-锅炉协调控制系统发电负荷-抽汽流量-机前压力整体控制品质较差，提出一种基于多智能体深度确定性策略梯度（MA-DDPG）的多工况自适应控制方法。首先，根据机组非线性动态机理建立计及关键状态参数变化的多个工况子模型，利用积分型函数切换机制获取最优工况子模型；其次，提出一种多智能体同步运行机制，以快速响应、稳定供热及安全运行为协调控制目标来设计奖励函数，通过训练智能体与环境进行互动，不断在线调整多回路增益，实现机组多工况自适应控制。仿真结果表明，与传统控制方法相比，所提方法在保证供热平稳性的同时，有效提升了机组大范围出力工况发电负荷响应速率。

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叶婧（1986），女，博士，讲师，主要研究方向为大规模新能源接入后电力系统优化运行与控制，yejing2000310@163.com。

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叶婧（1986），女，博士，讲师，主要研究方向为大规模新能源接入后电力系统优化运行与控制，yejing2000310@163.com。

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