Article(id=1308453951372812691, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.02.015, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755532800000, receivedDateStr=2025-08-19, revisedDate=1761753600000, revisedDateStr=2025-10-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889331906, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889331906, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889331906, creator=13701087609, updateTime=1789889331906, updator=13701087609, issue=Issue{id=1308453915104665783, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='2', pageStart='1', pageEnd='160', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789889323259, creator='13701087609', updateTime=1789889766020, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308455772237615687, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308455772237615688, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308453915104665783, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=120, endPage=128, ext={EN=ArticleExt(id=1308453951574139284, articleId=1308453951372812691, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Energy-Saving Optimization Method for Urban Rail Transit Flexible Traction Power Supply Systems Considering Multiple Load Scenarios, columnId=1152669334582243706, journalTitle=Urban Rapid Rail Transit, columnName=Electrical and Mechanical Engineering, runingTitle=null, highlight=null, articleAbstract=

Because the operating conditions of urban rail transit systems are highly variable and load fluctuations are severe, traditional energy-saving methods based on static scenarios struggle to accommodate dynamic load demands effectively. Therefore, load scenarios must be accurately identified, and an energy-saving optimization method for flexible traction power supply systems is needed to achieve dynamic "scenario-strategy" matching and improve energy-saving performance. Accordingly, an energy-saving optimization approach for urban rail transit flexible traction power supply systems considering multiple load scenarios is proposed. First, a k-Medoids load clustering method based on t-SNE is designed. A strategy of clustering after dimensionality reduction is adopted to more accurately reflect the correlations between system load and operating periods and seasons. Second, an energy-saving optimization model for the flexible traction power supply system is constructed. Based on an intelligent optimization algorithm, the virtual resistance of traction substations is adjusted to minimize the total incoming energy of the system. Through simulation validation, the effectiveness of the proposed method is verified. The proposed load clustering method effectively extracts the inherent characteristics of the load, reveals the correlation between system load and operating periods and seasons, and identifies six specific operating scenarios. The proposed energy-saving optimization method achieves good energy-saving performance in various operating scenarios, with a maximum energy-saving rate of 4.07%. The energy-saving optimization method for urban rail flexible traction power supply system based on load clustering has connected the complete chain of "scene recognition-strategy matching-dynamic adjustment", which can provide important technical support for energy-saving operation of urban rail transit.

, authors=Ming GAO^{1, 2}, Gang ZHANG^{1, 3}, Wei WEI¹, Renyu WANG¹, Baian TAO¹, Wei XIONG¹, authorsList=Ming GAO, Gang ZHANG, Wei WEI, Renyu WANG, Baian TAO, Wei XIONG, authorCompany=null, correspAuthors=null, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308453951913877909, articleId=1308453951372812691, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=多负荷场景的城轨柔性牵引供电系统节能优化方法, columnId=1152669334955536763, journalTitle=都市快轨交通, columnName=机电工程, runingTitle=null, highlight=null, articleAbstract=

城轨系统运行工况多变，负荷波动剧烈，基于静态场景的节能方法难以适配动态负荷需求，针对此问题，需准确划分负荷场景，研究柔性系统节能优化方法，实现“场景-策略”动态匹配，提升节能效果。为此，提出一种考虑多负荷场景的城轨柔性牵引供电系统节能优化方法。首先，设计基于t-SNE的k-Medoids负荷聚类方法，采取降维后聚类的策略，更精准地反映系统负荷与运行时段及季节间的关联。其次，构建柔性牵引供电系统节能优化模型，基于智能优化算法，以最小化系统总进线能量为目标，对牵引变电所的虚拟电阻进行调节。最后，对所提方法进行仿真验证，结果表明，所设计的负荷聚类方法有效挖掘负荷的内在特性，揭示系统负荷与运行时段及季节间的关联，明确系统运行的6种具体场景。该节能优化方法在各运行场景下皆能实现良好的节能效果，最高节能率可达4.07%。本文建立的“场景聚类-策略生成-节能调控”的节能优化框架，以期为城市轨道交通节能运行提供重要的技术支持。

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高明，男，学士，高级工程师，从事城市轨道交通牵引供电研究，13810935120@139.com

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