Converter transformer fault diagnosis method and system based on voiceprint and thermal signal

By extracting and fusing the time-frequency features of acoustic and thermal signals, and combining them with a random forest model, the problem of identifying multiple fault features of converter transformers was solved, achieving high-precision and rapid fault diagnosis and enhancing system safety.

CN121009329APending Publication Date: 2025-11-25STATE GRID HENAN ELECTRIC POWER ELECTRIC POWER SCI RES INST +2
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Patent Information

Application Number
CN202511103622.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-07
Publication Date
2025-11-25

AI Technical Summary

Technical Problem

Existing technologies are insufficient to effectively identify the various fault characteristics of converter transformers, and lack diagnostic models that combine acoustic fingerprint data with thermal temperature detection data, resulting in insufficient diagnostic accuracy and speed.

Method used

By acquiring acoustic and thermal signal data of converter transformers, time-frequency decomposition and feature extraction are performed. Features such as kurtosis, energy gradient, and entropy rate of change of the time-frequency matrix are calculated. The maximum mutual information coefficient is used to screen features, and fault diagnosis is performed in combination with a random forest model.

Benefits of technology

It enables accurate identification of various faults in converter transformers, improves diagnostic accuracy and speed, enhances noise and interference resistance, and improves fault risk perception capabilities.

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Abstract

The invention discloses a converter transformer fault diagnosis method and system based on voiceprints and thermal signals, and the method comprises the steps: carrying out the time-frequency decomposition of each voiceprint original signal data and temperature thermal signal data of a converter transformer, and obtaining a sound time-frequency matrix and a temperature time-frequency matrix; the kurtosis and the sound pulse duration of the time-frequency matrix of the sound are calculated to serve as basic time-frequency characteristics of the sound; calculating an energy gradient and an entropy change rate of the time-frequency matrix of the temperature as basic time-frequency characteristics; coupling characteristics of the time-frequency matrixes of the sound and the temperature in the same frequency band are extracted; performing feature screening on all basic time-frequency features and coupling features by calculating a maximum mutual information coefficient, and outputting an optimal feature subset; and constructing a converter transformer fault diagnosis-oriented identification model fusing ensemble learning and random forest, and training the identification model according to the optimal feature subsets in different operation states to perform fault diagnosis. The fault risk sensing capability of the converter transformer can be effectively improved.
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Citation Information

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