Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture
Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/1592426 |
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| author | Shihao Zhou Benren Pan Dongbin Lu Yiming Zhong Guannan Wang |
| author_facet | Shihao Zhou Benren Pan Dongbin Lu Yiming Zhong Guannan Wang |
| author_sort | Shihao Zhou |
| collection | DOAJ |
| description | Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. Then, a fault data set is constructed by finding effective information that can fully reflect the DC fault in the huge power grid environmental information, and the data set is screened, processed by classification feature fields, and linearly normalized. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed by introducing a deep convolutional neural network (DCNN) into the traditional generative adversarial network (GAN) for data training and DC fault diagnosis. In addition, the corresponding process is given. The proposed method and the other three methods are compared and analyzed by simulation experiments. The results show that the method proposed has the highest accuracy and smallest error loss value of 95.6% and 0.18, respectively. It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods. |
| format | Article |
| id | doaj-art-6c16b7a63bf64fe5b0a3ea315a1a8e99 |
| institution | OA Journals |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-6c16b7a63bf64fe5b0a3ea315a1a8e992025-08-20T02:06:11ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/1592426Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge ArchitectureShihao Zhou0Benren Pan1Dongbin Lu2Yiming Zhong3Guannan Wang4Electric Power Research Institute of State Grid Jiangxi Electric Power Co.Electric Power Research Institute of State Grid Jiangxi Electric Power Co.NR Electric Co.Electric Power Research Institute of State Grid Jiangxi Electric Power Co.Electric Power Research Institute of State Grid Jiangxi Electric Power Co.Aiming at the problem of fault diagnosis after the UHVDC system fails, a deep learning-based UHVDC fault diagnosis method under the cloud-edge architecture is proposed. First, based on the edge computing framework of the “cloud” + “edge terminal,” a four-layer fault diagnosis structure including the data integration layer, edge prediction layer, cloud diagnosis layer, and human-computer interaction layer is constructed. Then, a fault data set is constructed by finding effective information that can fully reflect the DC fault in the huge power grid environmental information, and the data set is screened, processed by classification feature fields, and linearly normalized. Finally, a deep convolutional generative adversarial network (DCGAN) is constructed by introducing a deep convolutional neural network (DCNN) into the traditional generative adversarial network (GAN) for data training and DC fault diagnosis. In addition, the corresponding process is given. The proposed method and the other three methods are compared and analyzed by simulation experiments. The results show that the method proposed has the highest accuracy and smallest error loss value of 95.6% and 0.18, respectively. It has the highest diagnosis accuracy under different fault types, and its performance is better than the other three comparison methods.http://dx.doi.org/10.1155/2022/1592426 |
| spellingShingle | Shihao Zhou Benren Pan Dongbin Lu Yiming Zhong Guannan Wang Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture Journal of Electrical and Computer Engineering |
| title | Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture |
| title_full | Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture |
| title_fullStr | Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture |
| title_full_unstemmed | Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture |
| title_short | Fault Diagnosis Method for UHVDC Transmission Based on Deep Learning under Cloud-Edge Architecture |
| title_sort | fault diagnosis method for uhvdc transmission based on deep learning under cloud edge architecture |
| url | http://dx.doi.org/10.1155/2022/1592426 |
| work_keys_str_mv | AT shihaozhou faultdiagnosismethodforuhvdctransmissionbasedondeeplearningundercloudedgearchitecture AT benrenpan faultdiagnosismethodforuhvdctransmissionbasedondeeplearningundercloudedgearchitecture AT dongbinlu faultdiagnosismethodforuhvdctransmissionbasedondeeplearningundercloudedgearchitecture AT yimingzhong faultdiagnosismethodforuhvdctransmissionbasedondeeplearningundercloudedgearchitecture AT guannanwang faultdiagnosismethodforuhvdctransmissionbasedondeeplearningundercloudedgearchitecture |