Internal insulation condition identification for high-voltage capacitor voltage transformers based on possibilistic...

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Condition Monitoring

Background:

• Internal insulation degradation in capacitor voltage transformers (CVTs) impacts measurement accuracy and operational safety.
• Aging and environmental factors accelerate CVT insulation deterioration, potentially leading to severe damage or failure.
• Effective monitoring and fault identification are essential for maintaining CVT reliability.

Purpose of the Study:

• To propose a data-driven method for identifying the internal insulation condition of CVTs.
• To develop a system capable of distinguishing between different types and degrees of insulation faults.
• To enhance the diagnostic capabilities for CVT internal insulation issues.

Main Methods:

• Collecting amplitude and phase data from CVT output voltages.
• Building recognition models integrating output voltage characteristics and substation distribution topology.
• Employing a possibilistic fuzzy clustering method for insulation condition monitoring.

Main Results:

• The proposed method effectively identifies various types and degrees of insulation faults in CVTs.
• Validation demonstrated successful identification of preset typical faults.
• The method also proved capable of diagnosing faults beyond those initially preset.

Conclusions:

• The data-driven approach offers a robust solution for monitoring CVT internal insulation.
• Accurate identification of fault types and degrees enhances predictive maintenance strategies for CVTs.
• The method provides reliable diagnostics, even for unforeseen insulation failure modes.