A method for identifying and separating chaotic flicker sources

By combining Lyapunov exponent and phase space reconstruction with multi-channel ICA algorithm, the chaotic flicker sources in the power system are identified and separated, which solves the problem of separating chaos and noise in power grid waveform distortion and achieves accurate positioning and separation of chaotic sources.

CN119622284BActive Publication Date: 2025-09-26HOHAI UNIV
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Patent Information

Application Number
CN202411662435.2
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-11-20
Publication Date
2025-09-26
Estimated Expiration
2044-11-20

AI Technical Summary

Technical Problem

Existing technologies are unable to effectively identify and separate chaotic flicker sources and noise in power systems, making it difficult to accurately analyze grid waveform distortion.

Method used

The Lyapunov index is used to judge chaotic motion, and the chaotic flicker source is identified by signal correlation ratio and phase space reconstruction, which is then separated by combining the multi-channel independent component analysis (ICA) algorithm.

Benefits of technology

The accurate location and separation of chaotic flicker sources and noise are achieved, providing an important reference for the study of harmonic source flicker in power systems.

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Abstract

The present invention belongs to the field of electric power technology and relates to a method for identifying and separating chaotic flicker sources. The method comprises the following steps: collecting observation values ​​of a nonlinear power system; judging that the nonlinear power system is a chaotic system by using a Lyapunov exponent; measuring an invariant measure of the chaotic system to obtain a distribution of a chaotic signal in a state space; using field test data and waveforms as references, determining a chaotic flicker source by using a signal-to-mixture ratio to measure short flicker values ​​when an ideal signal modulates chaotic signals of different amplitudes; reconstructing a phase space trajectory of a time delay embedding dimension using a nonlinear time series constructed using the observation values, and using the phase space trajectory to reconstruct the dynamic characteristics of the chaotic system to identify chaotic flicker source signals and noise; and separating the chaotic flicker source from the noise by using a multi-channel ICA separation algorithm; thereby providing support for accurately locating the chaotic source and the noise source.
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