Fault location of secondary equipment in smart substation based on GRU
Aiming at the problems of complex fault mechanism and difficult to adapt to the change of topology and fault characteristics for the secondary equipment of smart substation failure, a fault location method of the secondary equipment in smart substation based on gated recurrent unit (GRU) is proposed...
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-07-01
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| Series: | Diance yu yibiao |
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| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20230112007&flag=1&journal_id=dcyyb&year_id=2025 |
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| author | WANG Hongbin LI Zhi TONG Xiaoyang HUANG Ruiling ZHANG Tian |
| author_facet | WANG Hongbin LI Zhi TONG Xiaoyang HUANG Ruiling ZHANG Tian |
| author_sort | WANG Hongbin |
| collection | DOAJ |
| description | Aiming at the problems of complex fault mechanism and difficult to adapt to the change of topology and fault characteristics for the secondary equipment of smart substation failure, a fault location method of the secondary equipment in smart substation based on gated recurrent unit (GRU) is proposed. For the fault location objects such as merging unit, intelligent terminal, protection device, its sending and receiving network ports, and optical fibers, the alarm signals from related equipment are used when the secondary equipment fails to form an alarm signal set. Using the GRU network, each deep learning network fault location model for line, bus, and main transformer bays is established, and various training strategies for the secondary equipment fault location model are given. Through the case of a typical smart substation, and the effectiveness and accuracy of the proposed fault location method are verified by simulation experiments, and compared with the long short-term memory network and recurrent neural network, the proposed method can be more accurately and rapidly to locate secondary equipment in smart substation. |
| format | Article |
| id | doaj-art-03d9d511e6c8478dbb0ed2ea720e2d7f |
| institution | Kabale University |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-03d9d511e6c8478dbb0ed2ea720e2d7f2025-08-20T03:36:45ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-07-0162720020810.19753/j.issn1001-1390.2025.07.0231001-1390(2025)07-0200-09Fault location of secondary equipment in smart substation based on GRUWANG Hongbin0LI Zhi1TONG Xiaoyang2HUANG Ruiling3ZHANG Tian4State Grid Chongqing Electric Power Research Institute, Chongqing 401123, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaState Grid Chongqing Electric Power Research Institute, Chongqing 401123, ChinaChongqing Hechuan Sanfeng New Energy Power Generation Co., Ltd., Chongqing 401123, ChinaAiming at the problems of complex fault mechanism and difficult to adapt to the change of topology and fault characteristics for the secondary equipment of smart substation failure, a fault location method of the secondary equipment in smart substation based on gated recurrent unit (GRU) is proposed. For the fault location objects such as merging unit, intelligent terminal, protection device, its sending and receiving network ports, and optical fibers, the alarm signals from related equipment are used when the secondary equipment fails to form an alarm signal set. Using the GRU network, each deep learning network fault location model for line, bus, and main transformer bays is established, and various training strategies for the secondary equipment fault location model are given. Through the case of a typical smart substation, and the effectiveness and accuracy of the proposed fault location method are verified by simulation experiments, and compared with the long short-term memory network and recurrent neural network, the proposed method can be more accurately and rapidly to locate secondary equipment in smart substation.http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20230112007&flag=1&journal_id=dcyyb&year_id=2025smart substationsecondary equipmentfault locationalarm signalgated recurrent unit |
| spellingShingle | WANG Hongbin LI Zhi TONG Xiaoyang HUANG Ruiling ZHANG Tian Fault location of secondary equipment in smart substation based on GRU Diance yu yibiao smart substation secondary equipment fault location alarm signal gated recurrent unit |
| title | Fault location of secondary equipment in smart substation based on GRU |
| title_full | Fault location of secondary equipment in smart substation based on GRU |
| title_fullStr | Fault location of secondary equipment in smart substation based on GRU |
| title_full_unstemmed | Fault location of secondary equipment in smart substation based on GRU |
| title_short | Fault location of secondary equipment in smart substation based on GRU |
| title_sort | fault location of secondary equipment in smart substation based on gru |
| topic | smart substation secondary equipment fault location alarm signal gated recurrent unit |
| url | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20230112007&flag=1&journal_id=dcyyb&year_id=2025 |
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