Adaptive fault reflection method based on adjustable excitation frequency

By using an adaptive fault reflection method with adjustable excitation frequency, and by dynamically adjusting the excitation frequency using digital direct synthesis technology and particle swarm optimization algorithm, the problem of low signal-to-noise ratio in cable fault detection is solved, achieving high-precision fault identification and location, and adapting to different cable parameters and noise environments.

CN121596031APending Publication Date: 2026-03-03CHONGQING UNIV OF POSTS & TELECOMM
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Patent Information

Application Number
CN202511785125.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-01
Publication Date
2026-03-03

AI Technical Summary

Technical Problem

Existing cable fault detection methods suffer from fixed excitation frequencies, low signal-to-noise ratios, and poor matching efficiency, making it difficult to guarantee the optimal detection signal-to-noise ratio in complex noise environments and affecting positioning accuracy.

Method used

An adaptive fault reflection method with adjustable excitation frequency is adopted. An adjustable frequency excitation signal is generated through digital direct synthesis technology. Combined with an adjustable impedance matching network and particle swarm optimization algorithm, the excitation frequency is dynamically adjusted to maximize the signal-to-noise ratio, forming a closed-loop adaptive detection architecture.

Benefits of technology

It achieves high-precision fault identification and location under different cable parameters and noise environments, improves the sensitivity and adaptability of the detection system, and reduces reflection loss and interference.

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Abstract

The invention discloses a self-adaptive fault reflection method and device based on adjustable excitation frequency, and belongs to the technical field of cable fault detection and positioning. The method comprises the following steps: injecting an excitation signal which can be adjusted in a range of 1kHz-10MHz through an adjustable impedance matching network; collecting a reflected signal and calculating an SNR (f) curve; optimizing by adopting a particle swarm optimization algorithm and taking maximization of a signal-to-noise ratio as a target, and determining an optimal excitation frequency; and finally, calculating the fault position and type based on the optimal frequency. The particle swarm optimization algorithm is adopted to perform adaptive optimization on the excitation frequency, and the adjustable impedance matching network is combined to form a closed-loop adaptive detection framework of excitation-acquisition-analysis-optimization-re-excitation, so that the excitation frequency can be automatically adjusted according to a real-time detection result, and the detection accuracy is improved. And the fault detection sensitivity, the positioning precision and the energy efficiency are obviously improved.
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