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

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  • Jianfeng Zheng

    (School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
    Jiangsu Province Engineering Research Center of High-Level Energy and Power Equipment, 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
    Jiangsu Province Engineering Research Center of High-Level Energy and Power Equipment, Changzhou University, Changzhou 213164, China)

  • Weiyue Xu

    (School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou 213164, China
    Jiangsu Province Engineering Research Center of High-Level Energy and Power Equipment, Changzhou University, Changzhou 213164, China)

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%.

Suggested Citation

  • Jianfeng Zheng & Zhichao Chen & Qun Wang & Hao Qiang & Weiyue Xu, 2022. "GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network," Energies, MDPI, vol. 15(19), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7372-:d:935840
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    References listed on IDEAS

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    1. Yanxin Wang & Jing Yan & Zhou Yang & Tingliang Liu & Yiming Zhao & Junyi Li, 2019. "Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network," Energies, MDPI, vol. 12(24), pages 1-19, December.
    2. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, October.
    3. Simeng Song & Yong Qian & Hui Wang & Yiming Zang & Gehao Sheng & Xiuchen Jiang, 2020. "Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence," Energies, MDPI, vol. 13(16), pages 1-16, August.
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    Cited by:

    1. Songyuan Li & Pengxian Song & Zhanpeng Wei & Xu Li & Qinghua Tang & Zhengzheng Meng & Ji Li & Songtao Liu & Yuhuai Wang & Jin Li, 2022. "Partial Discharge Detection and Defect Location Method in GIS Cable Terminal," Energies, MDPI, vol. 16(1), pages 1-10, December.
    2. Aleksandra Płużek & Łukasz Nagi, 2022. "Classification of Partial Discharges Recorded by the Method Using the Phenomenon of Scintillation," Energies, MDPI, vol. 16(1), pages 1-9, December.
    3. Guang Wang & Jiale Xie & Shunli Wang, 2023. "Application of Artificial Intelligence in Power System Monitoring and Fault Diagnosis," Energies, MDPI, vol. 16(14), pages 1-3, July.

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