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Related Experiment Video

Updated: May 16, 2025

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Partial discharge defect recognition method of switchgear based on cloud-edge collaborative deep learning.

Zhijie Jia1, Songhai Fan2, Zhichuan Wang3

  • 1State Grid Sichuan Electric Power Research Institute, Chengdu, 610000, China. 17856626247@163.com.

Scientific Reports
|March 31, 2025
PubMed
Summary

This study introduces an edge computing and deep learning method for switchgear partial discharge (PD) recognition. The novel approach enhances real-time monitoring and defect identification accuracy, improving electrical equipment safety.

Keywords:
Deep belief networkEdge computingLocal linear embeddingPartial dischargeSwitchgear

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Power Systems

Background:

  • Traditional partial discharge (PD) detection methods for switchgear lack real-time monitoring, rapid assessment, and collaborative analysis capabilities.
  • Existing diagnostic models suffer from long training durations, low identification efficiency, and weak collaborative analysis.
  • There is a need for advanced methods to overcome the limitations of conventional PD detection in practical switchgear applications.

Purpose of the Study:

  • To propose a joint PD recognition method for switchgear utilizing edge computing and deep learning.
  • To develop an edge collaborative defect identification architecture for enhanced switchgear monitoring.
  • To improve the accuracy and efficiency of PD defect identification and real-time analysis in switchgear.

Main Methods:

  • An edge collaborative defect identification architecture comprising terminal device, collection, edge-computing, and cloud-computing sides was constructed.
  • PD signals were extracted using UHF and broadband pulse current sensors, with multidimensional features extracted and dimensionality reduction performed on the edge side.
  • A deep belief network (DBN) was employed on the cloud side for PD defect identification, with real-time training and model deployment to the edge for inference.

Main Results:

  • The proposed DBN method achieved an accuracy of 88.03% in recognizing PDs in switchgear.
  • The edge computing architecture reduced the training time of the switchgear PD defect type classifier by 44.28%.
  • The method facilitates real-time joint analysis of PD defects across multiple switchgear units.

Conclusions:

  • The proposed joint PD recognition method effectively addresses the limitations of traditional diagnostic models.
  • The integration of edge computing and deep learning (DBN) enhances the efficiency and accuracy of switchgear PD detection.
  • The developed architecture enables real-time monitoring, rapid assessment, and collaborative analysis of PD defects in switchgear.