Short-time fractional Fourier transform time-frequency analysis method based on window decoupling

Through the method of signal feature segmentation and virtual window interface, the problem of inflexible window function switching in non-stationary signal analysis under power quality disturbance in the power grid is solved, and more accurate time-frequency analysis and system scalability are achieved.

CN120446581AInactive Publication Date: 2025-08-08HUAIYIN TEACHERS COLLEGE
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Patent Information

Application Number
CN202510515135.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-23
Publication Date
2025-08-08
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

When the existing short-term fractional Fourier transform analyzes the non-stationary signals generated by the power quality disturbance in the power grid, it cannot flexibly switch the window function, resulting in inaccurate analysis results and poor system scalability, so the source program needs to be retested.

Method used

The signal is divided into several parts according to its characteristics, and a virtual window function is introduced to provide a program calling interface for specific window functions, allowing different window functions to be selected in different signal segments to avoid recompiling and testing.

Benefits of technology

It realizes accurate analysis of non-stationary signals of the power grid, improves the scalability and analysis efficiency of the system, and can flexibly adapt to signal characteristics changes.

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Abstract

The invention discloses a window-based decoupling short-time fractional Fourier transform time-frequency analysis method. The method comprises the following steps: S1, acquiring non-stationary signal data generated under power quality disturbance in a power grid; s2, segmenting the non-stationary signal into a plurality of parts based on signal waveform characteristics, and setting an initialized virtual window function for discrete short-time fractional Fourier transform, wherein the window function only provides an interface for calling of a specific window function; s3, calling different window functions for each segmented sub-segment signal to obtain windowed sub-segment signals under a specific window function; and S4, discrete fractional Fourier transform is applied to the sub-segment signals obtained in the step S3 to obtain time-frequency results of the sub-segment signals under different windows, and finally, frequency spectrums of the sub-segment signals are spliced according to a time sequence and output by an oscilloscope. Based on the characteristics of non-stationary signals generated under power quality disturbance in a power grid, different window function analysis signals can be called in one task by setting a virtual window function interface, and the window-based decoupling short-time fractional order Fourier transform time-frequency analysis method is formed. According to the method, the coupling between fractional Fourier transform and a specific window is reduced, the expandability of a program is improved, and more accurate signal local spectrum features can be obtained.
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