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A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals
Michał Kunicki1, Daria Wotzka2
1Institute of Electrical Power Engineering and Renewable Energy, Opole University of Technology, 45-758 Opole, Poland.
Sensors (Basel, Switzerland)
|December 5, 2019
Summary
This study introduces a novel acoustic method for identifying defects in power transformers. The approach accurately classifies partial discharges and other faults, enhancing transformer condition assessment.
Area of Science:
- Electrical Engineering
- Materials Science
- Acoustics
Background:
- Effective early detection of power transformer defects remains a significant challenge.
- Non-invasive and non-destructive testing methods are crucial for maintaining operational integrity.
- Acoustic emission (AE) is a recognized technique, primarily for partial discharge (PD) detection, but capable of identifying other anomalies.
Purpose of the Study:
- To develop and validate a new classification method for identifying defects in power transformers using acoustic measurements.
- To enhance the diagnostic capabilities of acoustic emission testing beyond partial discharge detection.
- To provide a supplementary tool for power transformer condition assessment and management.
Main Methods:
- Gathering a diverse database of acoustic signals from real-life power transformers over several years.
- Implementing a two-step classification strategy: first, distinguishing between partial discharges (PD) and other signal sources, and second, classifying into eight specific defect types.
- Utilizing machine learning algorithms trained and validated on energy patterns derived from discrete wavelet transform (DWT) of the acoustic signals.
Main Results:
- The proposed method achieved high accuracy in identifying specific types of partial discharges.
- The system successfully identified other types of faults and anomalies within the power transformers.
- The two-step classification approach demonstrated robust performance in defect identification.
Conclusions:
- The developed acoustic-based classification method offers a highly accurate and effective means for power transformer defect identification.
- This technique can serve as a valuable addition to existing technical condition assessment and decision support systems.
- The study highlights the potential of advanced signal processing and machine learning in improving power transformer reliability and safety.
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