Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems
Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fau...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/7/2937 |
| _version_ | 1827746939516485632 |
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| author | Xianglun Nie Jing Zhang Yu He Wenjian Luo Tingyun Gu Bowen Li Xiangxie Hu |
| author_facet | Xianglun Nie Jing Zhang Yu He Wenjian Luo Tingyun Gu Bowen Li Xiangxie Hu |
| author_sort | Xianglun Nie |
| collection | DOAJ |
| description | Fast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods. |
| first_indexed | 2024-03-11T05:39:17Z |
| format | Article |
| id | doaj.art-dfb80155a4e847ecbfe795988279dd61 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T05:39:17Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-dfb80155a4e847ecbfe795988279dd612023-11-17T16:35:10ZengMDPI AGEnergies1996-10732023-03-01167293710.3390/en16072937Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution SystemsXianglun Nie0Jing Zhang1Yu He2Wenjian Luo3Tingyun Gu4Bowen Li5Xiangxie Hu6College of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaElectric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, ChinaElectric Power Research Institute of Guizhou Power Grid Co., Ltd., Guiyang 550002, ChinaCollege of Electrical Engineering, Guizhou University, Guiyang 550025, ChinaFast and accurate fault detection is important for the long term, stable operation of the distribution network. For the resonant grounding system, the fault signal features extraction difficulties, and the existing detection method’s accuracy is not high. A ground fault detection method based on fault data stitching and image generation of resonant grounding distribution systems is proposed. Firstly, considering the correlation between the transient zero-sequence current (TZSC) of faulty and healthy feeders under the same operating conditions, a fault data stitching method is proposed, which splices the transient zero-sequence current signals of each feeder into system fault data, and then converts the system fault data into grayscale images by combining the signal-to-image conversion method. Then, an improved convolutional neural network (CNN) is used to train the grayscale images and then implement fault detection. The simulation results show that the proposed method has high accuracy and strong robustness compared with existing fault detection methods.https://www.mdpi.com/1996-1073/16/7/2937fault data stitchingimage generationconvolutional neural networkfault detectionfeature extractionfeature characterization capability |
| spellingShingle | Xianglun Nie Jing Zhang Yu He Wenjian Luo Tingyun Gu Bowen Li Xiangxie Hu Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems Energies fault data stitching image generation convolutional neural network fault detection feature extraction feature characterization capability |
| title | Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems |
| title_full | Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems |
| title_fullStr | Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems |
| title_full_unstemmed | Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems |
| title_short | Ground Fault Detection Based on Fault Data Stitching and Image Generation of Resonant Grounding Distribution Systems |
| title_sort | ground fault detection based on fault data stitching and image generation of resonant grounding distribution systems |
| topic | fault data stitching image generation convolutional neural network fault detection feature extraction feature characterization capability |
| url | https://www.mdpi.com/1996-1073/16/7/2937 |
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