Article(id=1322557092318270126, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322557042389275039, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.8.89, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1725465600000, receivedDateStr=2024-09-05, revisedDate=1735488000000, revisedDateStr=2024-12-30, acceptedDate=1738684800000, acceptedDateStr=2025-02-05, onlineDate=1793251782730, onlineDateStr=2026-10-29, pubDate=1788019200000, pubDateStr=2026-08-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793251782730, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793251782730, creator=13701087609, updateTime=1793251782730, updator=13701087609, issue=Issue{id=1322557042389275039, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='8', pageStart='1', pageEnd='354', issueExtLink='null', onlineDate='null', pubDate='1788019200000', pubDateStr='2026-08-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793251770826, creator='13701087609', updateTime=1793251770826, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=89, endPage=99, ext={EN=ArticleExt(id=1322557092548956847, articleId=1322557092318270126, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Low Switching Frequency Model Predictive Current Control of DFIG with Inductance Identification Function, columnId=1321491614980223555, journalTitle=Journal of Power Supply, columnName=Renewable Energy Generation and Energy Storage, runingTitle=null, highlight=null, articleAbstract=

Aimed at the problems of poor steady-state performance, high switching loss and susceptibility to changes in motor parameters of duty cycle model predictive current control, the low switching frequency model predictive current control strategy for a doubly-fed induction generator (DFIG) with an inductance identification function is studied. First, according to the principle of deadbeat control of rotor voltage, six expected voltage vectors are synthesized by distributing the action time of two adjacent basic voltage vectors and zero vectors. Second, a value function is constructed by taking the minimum rotor voltage prediction error and the minimum switching times at the switching points of two adjacent control cycles as the constraint conditions, and six vector combinations are enumerated and optimized. Finally, the d axis and q axis stator and rotor flux state equations are established. The model reference adaptive system (MRAS) algorithm is used to identify the inductance parameters in the motor system online, and the real-time identification results are fed back to the system. Simulation and experimental results show that the proposed strategy has good control performance and parameter robustness while effectively reducing the switching frequency.

, authors=Siyuan HUANG, Shuxi LIU, Ke LIU, Bo TANG, Zihao WANG, authorsList=Siyuan HUANG, Shuxi LIU, Ke LIU, Bo TANG, Zihao WANG, authorCompany=null, correspAuthors=Shuxi LIU, 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=1322557093106799282, articleId=1322557092318270126, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=具有电感辨识功能的双馈感应风力发电机低开关频率模型预测电流控制, columnId=1321491615269630533, journalTitle=电源学报, columnName=新能源发电与储能, runingTitle=null, highlight=null, articleAbstract=

针对占空比模型预测电流控制稳态性能不佳、开关损耗较高及易受电机参数变化影响等问题，研究了一种具有电感辨识功能的双馈感应风力发电机DFIG（doubly-fed induction generator）低开关频率模型预测电流控制策略。首先，根据转子电压无差拍控制原理，分配相邻2个基本电压矢量及零矢量作用时间，合成6个期望电压矢量。其次，以最小转子电压预测误差与相邻2个控制周期切换点处最小开关切换次数为约束条件构建价值函数，对6种矢量组合枚举寻优。最后，建立d轴与q轴定转子磁链状态方程，采用模型参考自适应系统MRAS（model reference adaptive system）算法对电机系统中的电感参数进行在线辨识，并将实时辨识结果反馈至系统中。仿真和实验结果表明，所提策略在有效降低开关频率的同时，具有良好的控制性能与参数鲁棒性。

, authors=黄思源, 刘述喜, 刘科, 唐博, 王子豪, authorsList=黄思源, 刘述喜, 刘科, 唐博, 王子豪, authorCompany=null, correspAuthors=刘述喜, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=CTUhMvP2e0CDPanb52sbMA==, magXml=USpmu8aNayFeE5qzhvnJmA==, pdfUrl=null, pdf=V7TCoByRH5TvLArXPeD7oA==, pdfFileSize=6572002, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=jKW6rqX/WWiQCst2K6W0Cw==, mapNumber=null, fund=null)}, authors=[Author(id=1322557093450732215, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322557092318270126, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=52220712188@stu.cqut.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1322557093534618297, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322557092318270126, authorId=1322557093450732215, language=EN, stringName=Siyuan HUANG, firstName=Siyuan, middleName=null, lastName=HUANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1322557093605921466, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1322557092318270126, authorId=1322557093450732215, language=CN, stringName=黄思源, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=重庆理工大学电气与电子工程学院，重庆　400054, bio={"content":"

黄思源（2001—　），女，硕士研究生。研究方向：双馈风力发电系统控制算法。E-mail：52220712188@stu.cqut.edu.cn。

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黄思源（2001—　），女，硕士研究生。研究方向：双馈风力发电系统控制算法。E-mail：52220712188@stu.cqut.edu.cn。

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刘科（2000—　），男，硕士研究生。研究方向：深度学习与电气设备故障诊断。E-mail：liuke666@stu.cqut.edu.cn。

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刘科（2000—　），男，硕士研究生。研究方向：深度学习与电气设备故障诊断。E-mail：liuke666@stu.cqut.edu.cn。

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唐博（1999—　），男，硕士研究生。研究方向：DAB变换器控制策略优化研究与应用。E-mail：cquttb@foxmail.com。

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唐博（1999—　），男，硕士研究生。研究方向：DAB变换器控制策略优化研究与应用。E-mail：cquttb@foxmail.com。

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王子豪（1998—　），男，硕士研究生。研究方向：双馈风力发电系统控制。E-mail：GrainYu@stu.cqut.edu.cn。

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王子豪（1998—　），男，硕士研究生。研究方向：双馈风力发电系统控制。E-mail：GrainYu@stu.cqut.edu.cn。

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