Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus
Abstract Partial discharge (PD) detection is used to evaluate the insulation status of high‐voltage equipment. The most challenging aspect of traditional PD recognition is extracting features from the discharge signal. Accordingly, this study applied the visual geometry group‐19 (VGG‐19) model to ga...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-06-01
|
| Series: | IET Science, Measurement & Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/smt2.12137 |
| _version_ | 1797813771302862848 |
|---|---|
| author | Feng‐Chang Gu |
| author_facet | Feng‐Chang Gu |
| author_sort | Feng‐Chang Gu |
| collection | DOAJ |
| description | Abstract Partial discharge (PD) detection is used to evaluate the insulation status of high‐voltage equipment. The most challenging aspect of traditional PD recognition is extracting features from the discharge signal. Accordingly, this study applied the visual geometry group‐19 (VGG‐19) model to gas‐insulated switchgear (GIS) PD image recognition. A high frequency current transformer and an LDP‐5 inductive sensor measured PD electrical signals emitted by 15‐kV GIS. Next, the Hilbert energy spectrum was obtained by Hilbert transform in the time and frequency domains. Compared with a phase‐resolved PD pattern, the Hilbert spectrum can represent the energy and instantaneous frequency with the time variable. Finally, the VGG‐19 model was applied for PD pattern recognition. For validation, its recognition performance was compared with that of a fractal theory by using a neural network method. The VGG‐19 method is straightforward and has a high PD recognition rate, thereby enabling equipment manufacturers to quickly verify the insulation of GIS during assembly or operation. |
| first_indexed | 2024-03-13T07:57:42Z |
| format | Article |
| id | doaj.art-001a0b0854de4904aff6d03de6840b58 |
| institution | Directory Open Access Journal |
| issn | 1751-8822 1751-8830 |
| language | English |
| last_indexed | 2024-03-13T07:57:42Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Science, Measurement & Technology |
| spelling | doaj.art-001a0b0854de4904aff6d03de6840b582023-06-02T03:16:00ZengWileyIET Science, Measurement & Technology1751-88221751-88302023-06-0117413714610.1049/smt2.12137Application of the convolutional neural network in partial discharge spectrum recognition of power apparatusFeng‐Chang Gu0Electrical Engineering National Chin‐Yi University of Technology Taiping Dist. Taichung TaiwanAbstract Partial discharge (PD) detection is used to evaluate the insulation status of high‐voltage equipment. The most challenging aspect of traditional PD recognition is extracting features from the discharge signal. Accordingly, this study applied the visual geometry group‐19 (VGG‐19) model to gas‐insulated switchgear (GIS) PD image recognition. A high frequency current transformer and an LDP‐5 inductive sensor measured PD electrical signals emitted by 15‐kV GIS. Next, the Hilbert energy spectrum was obtained by Hilbert transform in the time and frequency domains. Compared with a phase‐resolved PD pattern, the Hilbert spectrum can represent the energy and instantaneous frequency with the time variable. Finally, the VGG‐19 model was applied for PD pattern recognition. For validation, its recognition performance was compared with that of a fractal theory by using a neural network method. The VGG‐19 method is straightforward and has a high PD recognition rate, thereby enabling equipment manufacturers to quickly verify the insulation of GIS during assembly or operation.https://doi.org/10.1049/smt2.12137image recognitionpartial discharge measurementpower apparatus |
| spellingShingle | Feng‐Chang Gu Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus IET Science, Measurement & Technology image recognition partial discharge measurement power apparatus |
| title | Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus |
| title_full | Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus |
| title_fullStr | Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus |
| title_full_unstemmed | Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus |
| title_short | Application of the convolutional neural network in partial discharge spectrum recognition of power apparatus |
| title_sort | application of the convolutional neural network in partial discharge spectrum recognition of power apparatus |
| topic | image recognition partial discharge measurement power apparatus |
| url | https://doi.org/10.1049/smt2.12137 |
| work_keys_str_mv | AT fengchanggu applicationoftheconvolutionalneuralnetworkinpartialdischargespectrumrecognitionofpowerapparatus |