A power quality disturbance data detection method and device based on compressed sensing

By optimizing the SAMP algorithm using the fundamental wave filtering principle and growth curve function, the problems of large data volume and insufficient signal reconstruction accuracy in traditional power quality data acquisition methods are solved, achieving efficient and rapid power quality disturbance detection.

CN116973663BActive Publication Date: 2026-07-07NARI TECH CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NARI TECH CO LTD
Filing Date
2023-07-07
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Traditional power quality data acquisition methods based on the Nyquist sampling theorem result in large data volumes and difficulties in storage and processing. Existing compressed sensing algorithms such as OMP and CoSaMP are prone to overestimation of sparsity or excessive iterations when the signal sparsity is unknown, leading to low signal reconstruction accuracy and excessively long running time.

Method used

The fundamental and harmonic components of the signal are separated by a fundamental filtering principle. The sparsity of the signal is estimated by the slope curve and the growth curve function is used as a variable step size optimization SAMP algorithm to avoid overestimation of sparsity and improve the accuracy and efficiency of signal reconstruction.

Benefits of technology

It improves the accuracy and efficiency of power quality disturbance detection, enables rapid signal reconstruction, and can effectively detect transient events, thereby reducing algorithm runtime.

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Abstract

The application discloses a power quality disturbance data detection method and device based on compressed sensing. The method comprises the following steps: acquiring an original sampling signal, obtaining an observation vector through a measurement matrix to realize signal compression; separating a fundamental component and a harmonic component of the original signal according to the fundamental wave filtering principle; reconstructing the original signal according to the proportion of the harmonic component, arranging the inner product of a sensing matrix and a harmonic component observation vector in descending order when the harmonic component is more than a set condition, calculating a slope curve, estimating the signal sparsity according to the pole distribution of the slope curve, taking the sparsity estimation value as an initial step of a SAMP algorithm, and iteratively calculating a residual ratio threshold and a growth curve function to obtain a sparse vector estimation value of the harmonic component; and performing power quality disturbance data detection.
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