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Related Experiment Video

Updated: Jun 28, 2025

Extraction of the EPP Component from the Surface EMG
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Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System.

Itaiara Felix Carvalho1, Edson Guedes da Costa1, Luiz Augusto Medeiros Martins Nobrega1

  • 1Department of Electrical Engineering, Federal University of Campina Grande, Aprigio Veloso 882, Universitário, Campina Grande 58429-900, Brazil.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

This study introduces a new system for detecting, separating, and classifying partial discharges (PD) in substations using signal conditioning and machine learning. The method achieves high accuracy, enabling more reliable and cost-effective PD monitoring.

Keywords:
classification of partial dischargespartial dischargesignal conditioning system

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Area of Science:

  • Electrical Engineering
  • Power Systems Analysis
  • Signal Processing

Background:

  • Partial discharges (PD) pose a significant risk to substation equipment integrity.
  • Effective detection, separation, and classification of PD are crucial for preventing failures.
  • Existing methods may face challenges with sampling requirements and signal-to-noise ratio.

Purpose of the Study:

  • To develop an efficient system for detecting, separating, and classifying partial discharges in substations.
  • To reduce sampling requirements and enhance the signal-to-noise ratio for PD detection.
  • To utilize machine learning for accurate PD source separation and classification.

Main Methods:

  • Implementation of a signal conditioning system to reduce high-frequency components (up to 50 MHz).
  • Application of machine learning algorithms, including K-means, Gaussian Mixture Model (GMM), Mean-shift, and Support Vector Machine (SVM), for PD analysis.
  • Feature extraction from conditioned signals for classification.

Main Results:

  • The signal conditioning system successfully reduced high-frequency noise and improved the signal-to-noise ratio.
  • Effective separation of different partial discharge sources was achieved without information loss.
  • Classification accuracy of up to 93% for partial discharge sources was attained.

Conclusions:

  • The proposed signal conditioning and machine learning approach offers an effective solution for PD monitoring.
  • This system contributes to more economical, scalable, and reliable substation monitoring.
  • The findings pave the way for advanced PD diagnostic tools in power systems.