GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network

Jianfeng Zheng, Zhichao Chen, Qun Wang, Hao Qiang and Weiyue Xu ()
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Jianfeng Zheng: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Zhichao Chen: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Qun Wang: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Hao Qiang: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
Weiyue Xu: School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China

Energies, 2022, vol. 15, issue 19, 1-14

Abstract: 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%.

Keywords: partial discharge; time-frequency features; wavelet transform; convolutional neural network; pattern recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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