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Classification of Low Frequency Signals Emitted by Power Transformers Using Sensors and Machine Learning Methods.

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Summary

This study introduces an automated method for detecting and classifying low-frequency noise from power transformers using machine learning. This technique achieves over 97% accuracy in identifying transformer types and operational status through noise analysis.

Keywords:
classificationlow-frequency noiselow-frequency sensormachine learningpower transformer

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Area of Science:

  • Electrical Engineering
  • Acoustics
  • Machine Learning

Background:

  • Power transformers generate low-frequency noise during operation.
  • Accurate monitoring and classification of transformer noise are crucial for maintenance and diagnostics.
  • Existing methods may lack automation and precision in noise analysis.

Purpose of the Study:

  • To develop an automated method for detecting and classifying low-frequency noise from power transformers.
  • To optimize signal processing parameters for machine learning algorithms.
  • To evaluate the effectiveness of the proposed method across different transformer types.

Main Methods:

  • Utilizing sensors to capture sound pressure levels from operating transformers in real environments.
  • Applying frequency spectra analysis of the captured sound data.
  • Automatically optimizing frequency spectra interval and resolution for selected machine learning algorithms.
  • Testing various machine learning algorithms and optimization techniques on different transformer types (indoor and overhead).

Main Results:

  • The proposed method achieves over 97% accuracy in detecting and classifying transformer types based on low-frequency noise.
  • A preprocessing stage enhanced the method's accuracy by 10%.
  • Specific machine learning algorithms were identified as robust solutions for accurate noise classification.

Conclusions:

  • Automated detection and classification of transformer low-frequency noise is feasible with high accuracy.
  • The developed method enables efficient, non-invasive inspections of working transformers.
  • Optimized signal processing and machine learning are key to reliable transformer noise analysis.