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Article(id=1322531643584262997, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1322531607177707705, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2026.5.329, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1676563200000, receivedDateStr=2023-02-17, revisedDate=1700409600000, revisedDateStr=2023-11-20, acceptedDate=1702396800000, acceptedDateStr=2023-12-13, onlineDate=1793245715279, onlineDateStr=2026-10-29, pubDate=1780070400000, pubDateStr=2026-05-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793245715279, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793245715279, creator=13701087609, updateTime=1793245715279, updator=13701087609, issue=Issue{id=1322531607177707705, tenantId=1146029695717560320, journalId=1146031654075715584, year='2026', volume='24', issue='5', pageStart='1', pageEnd='348', issueExtLink='null', onlineDate='null', pubDate='1780070400000', pubDateStr='2026-05-30', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793245706600, creator='13701087609', updateTime=1793245706600, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=329, endPage=338, ext={EN=ArticleExt(id=1322531645949850455, articleId=1322531643584262997, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Open-circuit Fault Diagnosis of Grid-tied Inverters Based on Modified Random Forest, columnId=1321491668864455519, journalTitle=Journal of Power Supply, columnName=Fault Diagnosis and Reliability, runingTitle=null, highlight=null, articleAbstract=

Efficient diagnosis of IGBT open-circuit faults in grid-tied inverters is helpful for quickly detecting the fault and ensuring the stable operation of the grid-tied inverter system and the safety of equipment. The existing IGBT open-circuit fault diagnosis methods have problems such as difficult feature extraction and poor diagnosis accuracy. On this basis, a method is proposed for IGBT open-circuit fault diagnosis of grid-tied inverter based on modified random forest. First, the wavelet packet energy of each frequency band of three-phase current signal is extracted by maximum overlapping discrete wavelet (MODWT) decomposition as the characteristic values for IGBT open-circuit fault diagnosis; Then, the modified random forest method is used to identify different types of faults; Finally, the various IGBT open-circuit faults of grid-tied inverters are diagnosed. The experimental results show that the proposed method has higher diagnosis accuracy and is suitable for IGBT open-circuit fault diagnosis of grid-tied inverters compared with the traditional diagnosis methods such as back propagation (BP) neural network and support vector machine (SVM).

, authors=Ming ZHANG1, Qian ZHANG1, Xiao LI1, Xuan YE1, Shunfan HE2, authorsList=Ming ZHANG, Qian ZHANG, Xiao LI, Xuan YE, Shunfan HE, authorCompany=null, correspAuthors=Ming ZHANG, 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=1322531646805488472, articleId=1322531643584262997, tenantId=1146029695717560320, journalId=1146031654075715584, language=CN, title=一种改进随机森林算法的并网逆变器开路故障诊断方法, columnId=1321491668998673249, journalTitle=电源学报, columnName=故障诊断与可靠性, runingTitle=null, highlight=null, articleAbstract=

对并网逆变器IGBT开路故障高效诊断有助于快速发现故障，确保并网逆变系统的稳定运行和设备安全。现有IGBT开路故障诊断方法存在特征提取难、诊断正确率差等问题，为此提出了一种改进随机森林算法的并网逆变器IGBT开路故障诊断方法。首先利用极大重叠离散小波MODWT（maximum overlapping discrete wavelet）分解提取出三相电流信号各频带的小波能量，作为IGBT开路故障诊断用特征值；然后利用改进的随机森林算法识别不同类型的故障；最后对各种并网逆变器IGBT开路故障进行诊断实验。结果表明，相对于传统的反向传播BP（back propagation）神经网络、支持向量机SVM（support vector machine）等诊断方法，所提方法诊断正确率高，可适用于并网逆变器IGBT开路故障诊断。

, authors=张明1, 张倩1, 李效1, 叶轩1, 何顺帆2, authorsList=张明, 张倩, 李效, 叶轩, 何顺帆, authorCompany=null, correspAuthors=张明, authorNote=

张倩（1997—　），女，硕士研究生。研究方向：分布式新能源发电故障监测。E-mail：。

李效（1995—　），男，硕士研究生。研究方向：信号处理。E-mail：。

叶轩（1996—　），男，硕士研究生。研究方向：新型电力电子装置。E-mail：。

何顺帆（1984—　），男，博士，副教授。研究方向：电能质量分析与控制技术。E-mail：。

, correspAuthorsNote=
张明（1970—　），男，通信作者，博士，副教授。研究方向：分布式新能源发电故障监测。E-mail：。
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张倩（1997—　），女，硕士研究生。研究方向：分布式新能源发电故障监测。E-mail：。

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张倩（1997—　），女，硕士研究生。研究方向：分布式新能源发电故障监测。E-mail：。

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李效（1995—　），男，硕士研究生。研究方向：信号处理。E-mail：。

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李效（1995—　），男，硕士研究生。研究方向：信号处理。E-mail：。

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叶轩（1996—　），男，硕士研究生。研究方向：新型电力电子装置。E-mail：。

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叶轩（1996—　），男，硕士研究生。研究方向：新型电力电子装置。E-mail：。

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何顺帆（1984—　），男，博士，副教授。研究方向：电能质量分析与控制技术。E-mail：。

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何顺帆（1984—　），男，博士，副教授。研究方向：电能质量分析与控制技术。E-mail：。

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一种改进随机森林算法的并网逆变器开路故障诊断方法
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张明 1 , 张倩 1 , 李效 1 , 叶轩 1 , 何顺帆 2
电源学报 | 故障诊断与可靠性 2026,24(5): 329-338
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电源学报 |故障诊断与可靠性 2026 , 24 (5) : 329 -338
一种改进随机森林算法的并网逆变器开路故障诊断方法
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何顺帆（1984—　），男，博士，副教授。研究方向：电能质量分析与控制技术。E-mail：。

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何顺帆（1984—　），男，博士，副教授。研究方向：电能质量分析与控制技术。E-mail：。

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张明1 , 张倩1 , 李效1 , 叶轩1 , 何顺帆2
作者信息
  • 1.武汉纺织大学电子与电气工程学院，武汉　430200
  • 2.中南民族大学计算机科学学院，武汉　430074
通讯作者:
张明（1970—　），男，通信作者，博士，副教授。研究方向：分布式新能源发电故障监测。E-mail：。
作者简介:

张倩（1997—　），女，硕士研究生。研究方向：分布式新能源发电故障监测。E-mail：。

李效（1995—　），男，硕士研究生。研究方向：信号处理。E-mail：。

叶轩（1996—　），男，硕士研究生。研究方向：新型电力电子装置。E-mail：。

何顺帆（1984—　），男，博士，副教授。研究方向：电能质量分析与控制技术。E-mail：。

Open-circuit Fault Diagnosis of Grid-tied Inverters Based on Modified Random Forest
Ming ZHANG1 , Qian ZHANG1 , Xiao LI1 , Xuan YE1 , Shunfan HE2
Affiliations
  • 1.School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan 430200, China
  • 2.College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
出版时间: 2026-05-30 doi: 10.13234/j.issn.2095-2805.2026.5.329
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对并网逆变器IGBT开路故障高效诊断有助于快速发现故障，确保并网逆变系统的稳定运行和设备安全。现有IGBT开路故障诊断方法存在特征提取难、诊断正确率差等问题，为此提出了一种改进随机森林算法的并网逆变器IGBT开路故障诊断方法。首先利用极大重叠离散小波MODWT（maximum overlapping discrete wavelet）分解提取出三相电流信号各频带的小波能量，作为IGBT开路故障诊断用特征值；然后利用改进的随机森林算法识别不同类型的故障；最后对各种并网逆变器IGBT开路故障进行诊断实验。结果表明，相对于传统的反向传播BP（back propagation）神经网络、支持向量机SVM（support vector machine）等诊断方法，所提方法诊断正确率高，可适用于并网逆变器IGBT开路故障诊断。

并网逆变器  /  IGBT开路故障  /  改进随机森林  /  极大重叠离散小波能量

Efficient diagnosis of IGBT open-circuit faults in grid-tied inverters is helpful for quickly detecting the fault and ensuring the stable operation of the grid-tied inverter system and the safety of equipment. The existing IGBT open-circuit fault diagnosis methods have problems such as difficult feature extraction and poor diagnosis accuracy. On this basis, a method is proposed for IGBT open-circuit fault diagnosis of grid-tied inverter based on modified random forest. First, the wavelet packet energy of each frequency band of three-phase current signal is extracted by maximum overlapping discrete wavelet (MODWT) decomposition as the characteristic values for IGBT open-circuit fault diagnosis; Then, the modified random forest method is used to identify different types of faults; Finally, the various IGBT open-circuit faults of grid-tied inverters are diagnosed. The experimental results show that the proposed method has higher diagnosis accuracy and is suitable for IGBT open-circuit fault diagnosis of grid-tied inverters compared with the traditional diagnosis methods such as back propagation (BP) neural network and support vector machine (SVM).

Grid-tied inverters  /  IGBT open-circuit fault  /  modified random forest  /  MODWT energy
张明, 张倩, 李效, 叶轩, 何顺帆. 一种改进随机森林算法的并网逆变器开路故障诊断方法. 电源学报, 2026 , 24 (5) : 329 -338 . DOI: 10.13234/j.issn.2095-2805.2026.5.329
Ming ZHANG, Qian ZHANG, Xiao LI, Xuan YE, Shunfan HE. Open-circuit Fault Diagnosis of Grid-tied Inverters Based on Modified Random Forest[J]. Journal of Power Supply, 2026 , 24 (5) : 329 -338 . DOI: 10.13234/j.issn.2095-2805.2026.5.329
基金
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  • 国家自然科学基金资助项目(51477124; 61903384)
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2026年第24卷第5期
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文章信息
doi: 10.13234/j.issn.2095-2805.2026.5.329
  • 接收时间：2023-02-17
  • 首发时间：2026-10-29
  • 出版时间：2026-05-30
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出版历史
  • 收稿日期：2023-02-17
  • 修回日期：2023-11-20
  • 录用日期：2023-12-13
基金
National Natural Science Foundation of China(51477124; 61903384)
国家自然科学基金资助项目(51477124; 61903384)
作者信息
    1.武汉纺织大学电子与电气工程学院，武汉　430200
    2.中南民族大学计算机科学学院，武汉　430074

通讯作者:

张明（1970—　），男，通信作者，博士，副教授。研究方向：分布式新能源发电故障监测。E-mail：。
参考文献
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https://castjournals.cast.org.cn/joweb/dyxb/CN/10.13234/j.issn.2095-2805.2026.5.329
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