Characteristic Extraction and Assessment Methods for Transformers DC Bias Caused by Metro Stray Currents

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Transformer Health Monitoring

Background:

• Metro stray currents can lead to DC bias in transformers.
• Neutral DC levels fluctuate, making them unreliable sole indicators of DC bias risk.
• Existing methods may lack comprehensive assessment of transformer DC bias risk.

Purpose of the Study:

• To develop unified methods for extracting and assessing transformer DC bias risk from metro stray currents.
• To integrate transformer-neutral DC and vibration signals for a more robust risk evaluation.
• To establish an objective assessment model avoiding manual experience bias.

Main Methods:

• Characteristic extraction by comparing magnitude and frequency distributions of DC and vibration signals.
• Correlation analysis and clustering to identify key characteristic indicators.
• Application of the entropy weight method (EWM) for objective indicator weighting.
• Validation using real-world transformer neutral DC and vibration data.

Main Results:

• Successfully extracted characteristic indicators from combined DC and vibration signals.
• Demonstrated the feasibility of evaluating transformer DC bias risk using the proposed methods.
• The entropy weight method provided an objective weighting for risk assessment.

Conclusions:

• The proposed unified characteristic extraction and assessment methods effectively evaluate transformer DC bias risk caused by metro stray currents.
• Combining neutral DC and vibration signals offers a more reliable approach to risk assessment.
• The developed model provides an objective and data-driven solution for transformer monitoring.