Cable monitoring and ponding fault diagnosis system based on virtual simulation

Through the virtual simulation cable monitoring and water accumulation fault diagnosis system, the problem that traditional manual inspection is difficult to achieve real-time monitoring is solved, real-time monitoring and fault diagnosis of cable lines are realized, and the safety and reliability of the power system are improved.

CN119724235APending Publication Date: 2025-03-28XUANCHENG POWER SUPPLY OF ANHUI ELECTRIC POWER CORP
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Patent Information

Application Number
CN202411785125.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-06
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

Traditional manual inspections are difficult to achieve all-weather and comprehensive real-time monitoring of cable lines, which leads to difficult timely detection and handling of water accumulation or other fault problems, increasing the operating costs and safety risks of the power system.

Method used

A cable monitoring and water accumulation fault diagnosis system is adopted based on virtual simulation, which includes a signal generator, velocity sensor, voiceprint sensor and computer. Through finite element simulation and machine learning technology, a digital model of the cable trench environment is built to simulate the impact of water accumulation on the cable under different environmental conditions, and to achieve real-time monitoring and fault diagnosis of the cable.

Benefits of technology

Real-time monitoring and fault diagnosis of cable lines are realized, potential problems are discovered in advance, speed and accuracy of fault responses are improved, downtime and operation and maintenance costs of the power system are reduced, and safety and reliability of the power system are improved.

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Abstract

The invention discloses a cable monitoring and ponding fault diagnosis system based on virtual simulation, and relates to the technical field of voiceprint recognition, a signal generator generates an excitation signal and inputs the excitation signal to a computer software platform for simulation, and voiceprint conditions corresponding to different ponding distributions are obtained; collected sound signals are converted through short-time Fourier transform to generate a spectrogram, framing processing is carried out on the signals, Mel frequency spectrum and Mel cepstrum coefficient characteristic quantities are extracted, the influence of noise on voiceprint characteristics is researched through experiments, the noise is eliminated through multiple sensors and dynamic feed-forward compensation, and then an analytic hierarchy process and machine learning are adopted to improve the voiceprint characteristics. Voiceprint features highly related to the ponding amount are screened out, a ponding cable reflection voiceprint feature database is constructed, and cable monitoring and ponding fault diagnosis are achieved.
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