On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN
Box transformer substation (BTS) is an important power distribution environment. To ensure the safe and stable operation of the power distribution system, it is critical to monitor the BTS operation and diagnose its faults in a reliable manner. In the Internet of Things (IoT) environment, this paper...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2020-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/361350 |
| _version_ | 1797207073640939520 |
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| author | Erbao Xu* Yan Li Mingshun Yang Renhao Xiao Hairui Lin Xinqin Gao |
| author_facet | Erbao Xu* Yan Li Mingshun Yang Renhao Xiao Hairui Lin Xinqin Gao |
| author_sort | Erbao Xu* |
| collection | DOAJ |
| description | Box transformer substation (BTS) is an important power distribution environment. To ensure the safe and stable operation of the power distribution system, it is critical to monitor the BTS operation and diagnose its faults in a reliable manner. In the Internet of Things (IoT) environment, this paper aims to develop a real-time and accurate online strategy for BTS monitoring and fault diagnosis. The framework of our strategy was constructed based on the IoT technique, including a sensing layer, a network layer and an application layer. On this basis, a BTS fault diagnosis method was established with variable precision rough set (VPRS) as the pre-network and the radial basis function neural network (RBFNN) as the back-fed network. The VPRS and the RBFNN were selected, because the BTS faults have many characteristic parameters, with complex nonlinear relationship with fault modes. Finally, a prototype of our strategy was developed and applied to the fault diagnosis of an actual BTS. The results fully demonstrate the effectiveness and feasibility of our strategy. |
| first_indexed | 2024-04-24T09:17:07Z |
| format | Article |
| id | doaj.art-7700023e10994c38a0f7cda3d7ad5e9b |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:17:07Z |
| publishDate | 2020-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-7700023e10994c38a0f7cda3d7ad5e9b2024-04-15T16:40:53ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392020-01-012761965197310.17559/TV-20200916115647On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNNErbao Xu*0Yan Li1Mingshun Yang2Renhao Xiao3Hairui Lin4Xinqin Gao5School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, ChinaBox transformer substation (BTS) is an important power distribution environment. To ensure the safe and stable operation of the power distribution system, it is critical to monitor the BTS operation and diagnose its faults in a reliable manner. In the Internet of Things (IoT) environment, this paper aims to develop a real-time and accurate online strategy for BTS monitoring and fault diagnosis. The framework of our strategy was constructed based on the IoT technique, including a sensing layer, a network layer and an application layer. On this basis, a BTS fault diagnosis method was established with variable precision rough set (VPRS) as the pre-network and the radial basis function neural network (RBFNN) as the back-fed network. The VPRS and the RBFNN were selected, because the BTS faults have many characteristic parameters, with complex nonlinear relationship with fault modes. Finally, a prototype of our strategy was developed and applied to the fault diagnosis of an actual BTS. The results fully demonstrate the effectiveness and feasibility of our strategy.https://hrcak.srce.hr/file/361350box transformer substation (BTS)radial basis function neural network (RBFNN)the Internet of Things (IoT)variable precision rough set (VPRS) |
| spellingShingle | Erbao Xu* Yan Li Mingshun Yang Renhao Xiao Hairui Lin Xinqin Gao On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN Tehnički Vjesnik box transformer substation (BTS) radial basis function neural network (RBFNN) the Internet of Things (IoT) variable precision rough set (VPRS) |
| title | On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN |
| title_full | On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN |
| title_fullStr | On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN |
| title_full_unstemmed | On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN |
| title_short | On-Line Monitoring and Fault Diagnosis of Box Transformer Substation Based on VPRS-RBFNN |
| title_sort | on line monitoring and fault diagnosis of box transformer substation based on vprs rbfnn |
| topic | box transformer substation (BTS) radial basis function neural network (RBFNN) the Internet of Things (IoT) variable precision rough set (VPRS) |
| url | https://hrcak.srce.hr/file/361350 |
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