Article(id=1222499163288359196, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222499161174434087, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202306096, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1685548800000, receivedDateStr=2023-06-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769396113463, onlineDateStr=2026-01-26, pubDate=1695571200000, pubDateStr=2023-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769396113463, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769396113463, creator=13701087609, updateTime=1769396113463, updator=13701087609, issue=Issue{id=1222499161174434087, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='9', pageStart='1', pageEnd='204', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769396112960, creator=13701087609, updateTime=1769396581892, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222501128068129514, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222499161174434087, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222501128068129515, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222499161174434087, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=48, endPage=57, ext={EN=ArticleExt(id=1222499164181745954, articleId=1222499163288359196, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Load optimal distribution of deep peak regulation for thermal power units based on clustering, columnId=1222499161992323369, journalTitle=Thermal Power Generation, columnName=Clean, efficient and flexible coal-fired power technology, runingTitle=null, highlight=null, articleAbstract=

In order to provide methodological guidance for the load optimal distribution of thermal power units under the condition of deep peak regulation, the objective functions of optimal load distribution under medium-high load and low load are established respectively, and the load optimal distribution under different conditions is studied. The k-means clustering algorithm is used to process the data and the coal consumption characteristic curves of two units under medium-high load and low load are fitted. The objective function of load distribution is established to compare the coal consumption before and after optimization under static load distribution, incremental load distribution and continuous variable load conditions. After setting the total load instructions of the two units under the condition of deep peak shaving, the coal consumption of the two schemes of load average distribution and optimal distribution is compared. The results show that the clustering-based objective function models of optimal distribution under medium-high load and low load established by the coal consumption characteristic curve are reliable, whose optimization effect is remarkable under the condition of deep peak regulation. The k-means clustering algorithm can be used to optimize the load distribution of thermal power units and provide reference for the study of load optimal distribution under the condition of deep peak regulation of thermal power units.

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为了给深度调峰工况下火电机组负荷优化分配提供方法指导，分别建立了机组中高负荷与低负荷下的优化分配目标函数，并对不同状态下的负荷优化分配进行了研究。利用k均值聚类算法处理数据，拟合出2台机组中高负荷与低负荷下的煤耗特性曲线；建立负荷分配目标函数，对比负荷静态分配、增量分配及持续变负荷条件下优化前后的煤耗量；设定深度调峰工况下2台机组总负荷指令，对比负荷平均分配和优化分配2种方案的煤耗量。结果表明：利用基于聚类的煤耗特性曲线建立中高负荷与低负荷下的优化分配目标函数，方法可靠，深度调峰工况下优化效果显著。k均值聚类算法可用于火电机组负荷优化分配，并可为火电机组深度调峰工况下负荷优化分配的研究提供参考。

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任燕燕（1983），女，博士，讲师，主要研究方向为热力系统建模、控制与优化，yyren0812@cumt.edu.cn。

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任燕燕（1983），女，博士，讲师，主要研究方向为热力系统建模、控制与优化，yyren0812@cumt.edu.cn。

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