Article(id=1221497397583201273, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202206115, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1655136000000, receivedDateStr=2022-06-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769157273907, onlineDateStr=2026-01-23, pubDate=1682352000000, pubDateStr=2023-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769157273907, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769157273907, creator=13701087609, updateTime=1769157273907, 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=135, endPage=143, ext={EN=ArticleExt(id=1221497397893578769, articleId=1221497397583201273, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Research on optimization of incoming coal stacking in power station coal yard based on K-means clustering algorithm, columnId=1211002409397129992, journalTitle=Thermal Power Generation, columnName=Power generation technology forum, runingTitle=null, highlight=null, articleAbstract=

In order to effectively deal with the complex electricity and coal market, strengthening the smart fuel management has become an important part of thermal power plant management. Aiming at solving the problems that the coal yard of a coal-fired power station occupies small area, the types of incoming coal are complex, and the coal-fired coal stacking is chaotic, by extracting the coal quality information of historical incoming coal, K-means and DBSCAN clustering algorithms are used to analyze the low-level coal. The calorific value, volatile matter and sulfur content are clustered and analyzed, and the two clustering algorithms are compared from the perspective of silhouette coefficient, cluster stability and sample division fineness, and finally K-means with better clustering effect is selected as the calculation method for coal quality division. The K-means algorithm divides the selected historical coal quality information data set into four categories, the contour coefficient is 0.587, and the coal quality components in each category are similar. The incoming coal frequency and the incoming coal weight ratio under different cluster labels are counted, and the coal yard is divided into corresponding proportions. The incoming coal of the same classification is stacked in each partition, and on this basis, the incoming coal in the digital coal yard platform is designed. Coal stacking guidance and information storage process are of great significance to improving the utilization of storage yard space and the efficiency of coal yard management.

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为有效应对复杂的电力和煤炭市场，加强燃料智慧管理成为火电厂管理的重要组成部分。针对某燃煤电厂的煤场占地面积小、来煤煤种复杂和燃煤堆放混乱的问题，通过提取历史来煤的煤质信息，使用K-means和DBSCAN 2种聚类算法对来煤低位发热量、挥发分、硫分进行聚类分析，从轮廓系数、聚类稳定性和样本划分精细度3个方面对2种聚类算法进行对比，最终选择聚类效果更好的K-means聚类算法作为煤质划分的计算方法。K-means聚类算法将选取的历史煤质信息数据集划分为4类，轮廓系数为0.587，且划分后的同一类别内煤质成分相近。统计不同聚类标签下的来煤频率和来煤质量比例，对煤场进行了相应的比例划分，每一分区堆放相同分类的来煤，并以此为基础设计了数字化煤场平台中的来煤堆放指导及信息存储流程，对提高堆场空间利用率和煤场管理效率具有重要的意义。

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安吉振（1997），男，硕士研究生，主要研究方向为电力大数据分析及智能优化、设备故障诊断及智能预警，ajz@ncepu.edu.cn。

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安吉振（1997），男，硕士研究生，主要研究方向为电力大数据分析及智能优化、设备故障诊断及智能预警，ajz@ncepu.edu.cn。

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