Residual current recognition based on adaptive VMD and optimized DFNN
In order to realize rapid fault recognition of residual current device (RCD) and improve power safety, a fault residual current recognition method (AVMD-DFNN) based on adaptive variational modal decomposition (AVMD) and optimal dynamic fuzzy neural network (DFNN) is proposed. The decomposition param...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-03-01
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| Series: | Diance yu yibiao |
| Subjects: | |
| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220608007&flag=1&journal_id=dcyyb&year_id=2025 |
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| author | ZHANG Xiangke WANG Yajing DOU Zhenhai BAI Yunpeng WANG Wei |
| author_facet | ZHANG Xiangke WANG Yajing DOU Zhenhai BAI Yunpeng WANG Wei |
| author_sort | ZHANG Xiangke |
| collection | DOAJ |
| description | In order to realize rapid fault recognition of residual current device (RCD) and improve power safety, a fault residual current recognition method (AVMD-DFNN) based on adaptive variational modal decomposition (AVMD) and optimal dynamic fuzzy neural network (DFNN) is proposed. The decomposition parameters of VMD are determined adaptively by empirical mode decomposition (EMD) to realize the de-noising of the residual current signal. The characteristic parameters of residual current signal are extracted and used as the classification index of DFNN to recognize the type of residual current fault after the dimensionality reduction process. The DFNN is optimized by the minimum output method to remove the redundant fuzzy rule functions, so as to realize the rapid fault recognition of RCD. The simulation results show that AVMD-DFNN has high recognition accuracy and speed, which provides a theoretical reference for the development of new adaptive residual current devices. |
| format | Article |
| id | doaj-art-a56a3a34f23149f1a64d2938ed910a3b |
| institution | Kabale University |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-03-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-a56a3a34f23149f1a64d2938ed910a3b2025-08-20T03:42:43ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-03-0162319019710.19753/j.issn1001-1390.2025.03.0231001-1390(2025)03-0190-08Residual current recognition based on adaptive VMD and optimized DFNNZHANG Xiangke0WANG Yajing1DOU Zhenhai2BAI Yunpeng3WANG Wei4School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, Shandong, ChinaSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, Shandong, ChinaSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, Shandong, ChinaSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, Shandong, ChinaSchool of Electrical and Electronic Engineering, Shandong University of Technology, Zibo 255000, Shandong, ChinaIn order to realize rapid fault recognition of residual current device (RCD) and improve power safety, a fault residual current recognition method (AVMD-DFNN) based on adaptive variational modal decomposition (AVMD) and optimal dynamic fuzzy neural network (DFNN) is proposed. The decomposition parameters of VMD are determined adaptively by empirical mode decomposition (EMD) to realize the de-noising of the residual current signal. The characteristic parameters of residual current signal are extracted and used as the classification index of DFNN to recognize the type of residual current fault after the dimensionality reduction process. The DFNN is optimized by the minimum output method to remove the redundant fuzzy rule functions, so as to realize the rapid fault recognition of RCD. The simulation results show that AVMD-DFNN has high recognition accuracy and speed, which provides a theoretical reference for the development of new adaptive residual current devices.http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220608007&flag=1&journal_id=dcyyb&year_id=2025residual currentdynamic fuzzy neural networkvariational mode decompositionfault recognition |
| spellingShingle | ZHANG Xiangke WANG Yajing DOU Zhenhai BAI Yunpeng WANG Wei Residual current recognition based on adaptive VMD and optimized DFNN Diance yu yibiao residual current dynamic fuzzy neural network variational mode decomposition fault recognition |
| title | Residual current recognition based on adaptive VMD and optimized DFNN |
| title_full | Residual current recognition based on adaptive VMD and optimized DFNN |
| title_fullStr | Residual current recognition based on adaptive VMD and optimized DFNN |
| title_full_unstemmed | Residual current recognition based on adaptive VMD and optimized DFNN |
| title_short | Residual current recognition based on adaptive VMD and optimized DFNN |
| title_sort | residual current recognition based on adaptive vmd and optimized dfnn |
| topic | residual current dynamic fuzzy neural network variational mode decomposition fault recognition |
| url | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220608007&flag=1&journal_id=dcyyb&year_id=2025 |
| work_keys_str_mv | AT zhangxiangke residualcurrentrecognitionbasedonadaptivevmdandoptimizeddfnn AT wangyajing residualcurrentrecognitionbasedonadaptivevmdandoptimizeddfnn AT douzhenhai residualcurrentrecognitionbasedonadaptivevmdandoptimizeddfnn AT baiyunpeng residualcurrentrecognitionbasedonadaptivevmdandoptimizeddfnn AT wangwei residualcurrentrecognitionbasedonadaptivevmdandoptimizeddfnn |