Partial discharge signal detection and segmentation method and system based on wavelet bayesian threshold

By employing wavelet Bayesian thresholding and zero-phase filtering techniques, adaptive noise reduction and automatic segmentation of partial discharge signals from high-voltage cables are achieved. This solves the problems of adaptability and accuracy in partial discharge signal processing in existing technologies and is suitable for practical engineering needs in high-voltage cable insulation monitoring.

CN122430656BActive Publication Date: 2026-08-28STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST
2 Cites 0 Cited by

Patent Information

Application Number
CN202610886819.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-06-18
Publication Date
2026-08-28
Estimated Expiration
2046-06-18

AI Technical Summary

Technical Problem

Existing technologies for processing partial discharge signals from high-voltage cables suffer from weak noise reduction adaptability, low wavefront detection accuracy, inability to automatically complete the regular segmentation of partial discharge events, and difficulty in adapting to the processing requirements of low signal-to-noise ratio partial discharge signals in the field, leading to problems such as noise misjudgment, missed detection of weak pulses, and the mixing of interference segments.

Method used

Adaptive noise reduction is achieved by combining translation-invariant wavelet transform with Bayesian adaptive thresholding, and interference signals are removed by combining a cascaded fourth-order zero-phase Butterworth high-pass filter. Wavefront position is identified by sliding window energy comparison, and partial discharge waveform is automatically truncated based on the expanded sampling interval to achieve automatic signal segmentation.

Benefits of technology

It improves the recognition and positioning accuracy of partial discharge signals, and can stably achieve noise reduction, precise wavefront positioning and automatic event segmentation of partial discharge signals under low signal-to-noise ratio and strong interference conditions, making it suitable for a variety of engineering application scenarios.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The application discloses a partial discharge signal detection and segmentation method and system based on a wavelet Bayesian threshold, and the method comprises the following steps: through the combination of a shift-invariant wavelet transform and a Bayesian adaptive threshold, adaptive noise reduction is performed on a collected first partial discharge signal to suppress white noise and electromagnetic interference and avoid a pseudo Gibbs effect; then, a cascaded fourth-order zero-phase Butterworth high-pass filter is used to eliminate a direct current component and low-frequency clutter and completely retain pulse front characteristics; then, square amplitude enhancement and adaptive threshold screening are sequentially performed on the filtered signal to highlight effective pulses and eliminate slight interference; finally, through sliding window energy analysis, the starting position of a pulse wave front and the ending position of a pulse are accurately identified, and a reserved expansion sampling interval is combined to automatically intercept a complete partial discharge waveform, so that the automatic segmentation of a partial discharge event can be realized without relying on manual operation, and the recognition and positioning accuracy of the partial discharge signal is improved.
Need to check novelty before this filing date? Find Prior Art