Use of a Duffing chaotic oscillator with nonlinear stochastic resonance to retrieve faulty power equipment records
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Changzhui Lin
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Chuanxiang Peng
Abstract
Fault detection becomes especially crucial for maintaining the dependability and safety of power equipment as power systems become more complicated. This work aims to increase the accuracy and efficiency of fault signal identification in power equipment by proposing a method based on nonlinear stochastic resonance and a Duffing chaotic oscillator to detect weak signals in power systems. First, this study develops a weak signal detection model based on the Duffing chaotic oscillator approach. By modifying the system parameters to put the signal in a critical condition, the model greatly increases the sensitivity of the signal to the tiny periodic sinusoidal waveforms. In the meantime, the stochastic resonance method’s high resistance to noise interference improves the dependability of signal identification. The experimental results demonstrate that the approach is more accurate and stable than the conventional method and can extract fault signal features when working with weak signals in high noise conditions. Ultimately, this paper’s research offers a fresh, efficient method for identifying power system faults.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Changzhui Lin, Chuanxiang Peng, is responsible for designing the frame work, analyzing the performance, validating the results, and writing the article. Bingqian Chen,Nuo Cheng, is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: Authors do not have any conflicts.
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Research funding: Research on the Implementation Pathway and Technology Genealogy of the Original Technology Source for State Grid Fujian Economic Research Institute (2024).
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Data availability: No datasets were generated or analyzed during the current study.
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