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.

CN120507707BActive Publication Date: 2025-09-12DALIAN ZHONGGUANG INSTR TRANSFORMER
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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

Technical Problem

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.

Method used

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.

Benefits of technology

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.

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Abstract

The present invention relates to the field of electrical variable evaluation technology, and in particular to a low-impedance voltage transformer evaluation method based on real-time data adaptive drive. The method comprises the following steps: generating a real-time transformer data set by real-time acquisition of the transformer primary-side electrical signal and the transformer secondary-side electrical signal corresponding to the low-impedance voltage transformer, and synchronously acquiring the corresponding temperature, humidity and vibration data; performing dynamic quality evaluation screening and variational modal feature extraction on the transformer real-time data set to obtain a transformer dynamic feature vector; constructing a transformer adaptive evaluation model based on the transformer dynamic feature vector and evaluating and generating transformer real-time evaluation status data; obtaining historical fault sample data corresponding to the low-impedance voltage transformer and performing real-time comparison fault warning to generate a real-time status fault warning signal corresponding to the low-impedance voltage transformer. The present invention can improve the evaluation efficiency of the operating status of a low-impedance voltage transformer.
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