GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network
Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusi...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/19/7372 |
| _version_ | 1797479447100653568 |
|---|---|
| author | Jianfeng Zheng Zhichao Chen Qun Wang Hao Qiang Weiyue Xu |
| author_facet | Jianfeng Zheng Zhichao Chen Qun Wang Hao Qiang Weiyue Xu |
| author_sort | Jianfeng Zheng |
| collection | DOAJ |
| description | Different types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%. |
| first_indexed | 2024-03-09T21:46:00Z |
| format | Article |
| id | doaj.art-6e461938b76a476cbb90f123ef27a359 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:46:00Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6e461938b76a476cbb90f123ef27a3592023-11-23T20:17:55ZengMDPI AGEnergies1996-10732022-10-011519737210.3390/en15197372GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural NetworkJianfeng Zheng0Zhichao Chen1Qun Wang2Hao Qiang3Weiyue Xu4School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaSchool of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, ChinaDifferent types of partial discharge (PD) in gas-insulated switchgear (GIS) cause different damage to GIS insulation, correctly identifying the PD type is very important for evaluating the insulation status of GIS. This paper proposes a PD pattern recognition method based on an improved feature fusion convolutional neural network (IFCNN) to fully use the time-frequency features of PD pulses to realize PD pattern recognition. Firstly, the one-dimensional time-domain feature sequence of the PD pulse and the corresponding wavelet time-frequency diagram are applied as inputs. Secondly, the convolutional neural network (CNN) with two parallel channels is used for feature extraction, the extracted fault information is fused, and the shallow features of the wavelet time-frequency diagram are fused to prevent feature loss caused by pooling operation. Finally, the extracted features are sent to the classifier to recognize different types of PD. The discharge data of different types of PD are obtained for testing by experiments and simulation. Compared with 1-D CNN and 2-D CNN under the same specification, the proposed method can mine more potential local features of discharge pulses by fusing the time-frequency features of PD pulses in different dimensions, and improves the recognition accuracy to 95.8%.https://www.mdpi.com/1996-1073/15/19/7372partial dischargetime-frequency featureswavelet transformconvolutional neural networkpattern recognition |
| spellingShingle | Jianfeng Zheng Zhichao Chen Qun Wang Hao Qiang Weiyue Xu GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network Energies partial discharge time-frequency features wavelet transform convolutional neural network pattern recognition |
| title | GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network |
| title_full | GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network |
| title_fullStr | GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network |
| title_full_unstemmed | GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network |
| title_short | GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network |
| title_sort | gis partial discharge pattern recognition based on time frequency features and improved convolutional neural network |
| topic | partial discharge time-frequency features wavelet transform convolutional neural network pattern recognition |
| url | https://www.mdpi.com/1996-1073/15/19/7372 |
| work_keys_str_mv | AT jianfengzheng gispartialdischargepatternrecognitionbasedontimefrequencyfeaturesandimprovedconvolutionalneuralnetwork AT zhichaochen gispartialdischargepatternrecognitionbasedontimefrequencyfeaturesandimprovedconvolutionalneuralnetwork AT qunwang gispartialdischargepatternrecognitionbasedontimefrequencyfeaturesandimprovedconvolutionalneuralnetwork AT haoqiang gispartialdischargepatternrecognitionbasedontimefrequencyfeaturesandimprovedconvolutionalneuralnetwork AT weiyuexu gispartialdischargepatternrecognitionbasedontimefrequencyfeaturesandimprovedconvolutionalneuralnetwork |