Adaptive wavelet optimization and feature extraction method and system for transformer sound signals

Through the adaptive wavelet optimization and feature extraction method, the noise variance calculation model and dynamic threshold function are improved by using the M estimator for denoising. Combined with the support vector machine recursive feature elimination method and principal component analysis algorithm, the contradiction between noise suppression and signal fidelity in transformer acoustic monitoring is solved, and the accuracy of fault recognition is improved.

CN120492912AActive Publication Date: 2025-08-15SHANGHAI JUNSHI ELECTRICAL TECH +1
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Patent Information

Application Number
CN202510992274.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-18
Publication Date
2025-08-15
Estimated Expiration
2045-07-18

AI Technical Summary

Technical Problem

The existing transformer acoustic monitoring technology has problems such as low recognition accuracy, susceptible to environmental noise interference, and unsatisfactory noise reduction effect, which is difficult to meet the needs of modern power systems for intelligent and refined monitoring.

Method used

Adaptive wavelet optimization and feature extraction methods are used to improve the noise variance calculation model, dynamic threshold function and adaptive correction factor for denoising through M estimation. Combined with the support vector machine recursive feature elimination method and principal component analysis algorithm, the feature matrix of the transformer acoustic signal is extracted.

Benefits of technology

It effectively improves the accuracy of transformer fault recognition, solves the problem of noise suppression and signal fidelity contradiction, and high-dimensional feature redundancy, resulting in low recognition rate, and improves the accuracy of fault diagnosis.

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Abstract

The invention discloses a transformer sound signal adaptive wavelet optimization and feature extraction method and system, and the method comprises the steps: calling a Pywt wavelet analysis library, decomposing an original signal according to a decomposition layer number J, and obtaining a multi-layer detail signal; for each layer of detail signals, the following steps are executed: introducing an M estimator to improve a noise variance calculation model, and calculating a standard deviation and a unified monitoring threshold value; constructing a dynamic threshold value based on a denoising signal approximation error minimization criterion; designing a correction factor; correcting the wavelet coefficient of each layer of detail signal; reconstructing a pure signal by using an inverse decomposition method; dividing the pure signal into a plurality of short-time signals; extracting an MFCC feature vector of each short-time signal; weighting and screening MFCC feature vectors by adopting a support vector machine recursive feature elimination method; and compressing the dimension of the feature vector in combination with a principal component analysis algorithm to generate a final feature matrix. According to the method, the problems of contradiction between noise suppression and signal fidelity and low recognition rate caused by high-dimensional feature redundancy in a traditional method are solved.
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