Low-impedance voltage transformer evaluation method based on real-time data adaptive drive
The electrical signals of the transformer are collected in real time through Rogowski coils and resistor dividers. Combined with temperature, humidity and vibration sensor data, real-time data set generation and quality screening are performed. By using variational modal feature extraction and adaptive evaluation models, the problem that low-impedance voltage transformer evaluation methods cannot reflect their dynamic conditions in real time is solved, and efficient equipment status monitoring and fault warning are achieved.
Patent Information
- Application Number
- CN202510977063.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-16
- Publication Date
- 2025-09-12
- Estimated Expiration
- 2045-07-16
AI Technical Summary
Existing low-impedance voltage transformer evaluation methods cannot reflect their working performance under different dynamic conditions in real time and comprehensively, resulting in low evaluation efficiency.
The electrical signals of the transformer are collected in real time through Rogowski coils and resistor dividers. Combined with the temperature, humidity and vibration sensor data, real-time data set generation and quality screening are performed. The dynamic feature vector extraction and fault warning are realized by using variational modal feature extraction and adaptive evaluation models.
It achieves real-time and comprehensive evaluation of low-impedance voltage transformers, improves evaluation efficiency and fault diagnosis accuracy, and ensures equipment health status monitoring and maintenance efficiency.