Substation main transformer equipment operation state monitoring system based on voiceprint recognition

By combining voiceprint recognition technology with a multi-dimensional parameter monitoring system, the problem of early fault identification of main transformers in substations has been solved, enabling accurate monitoring and early warning, and improving the accuracy of equipment status assessment and operation and maintenance efficiency.

CN121565183APending Publication Date: 2026-02-24STATE GRID ZHEJIANG ELECTRIC POWER CO LTD LINHAI POWER SUPPLY CO
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Patent Information

Application Number
CN202511813816.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-04
Publication Date
2026-02-24

AI Technical Summary

Technical Problem

Existing technologies are insufficient to accurately identify early faults in substation main transformers, manual inspections are prone to missing faults, and the detection results depend on personnel experience and affect the normal operation of equipment.

Method used

A monitoring system based on voiceprint recognition is adopted. Voiceprint signals are collected through directional sound sensors, and data is processed by combining multi-dimensional parameters, wavelet threshold noise reduction and timestamp synchronization algorithms. A normal voiceprint baseline library is constructed by combining a deep learning model, fault characteristics are identified in real time, and full-area monitoring and early warning are realized through edge computing and the Internet of Things.

Benefits of technology

It enables accurate fault identification and early warning of main transformers in substations, reduces data transmission latency, ensures the integrity and accuracy of monitoring, and improves the response speed of operation and maintenance personnel and the safety of equipment.

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Abstract

The invention relates to the technical field of equipment operation state monitoring, in particular to a transformer substation main transformer equipment operation state monitoring system based on voiceprint recognition. Comprising a voiceprint collection module, a data preprocessing module, a voiceprint analysis module, a state evaluation module, a cooperative monitoring module, an interaction early warning module, a block chain evidence storage module and an operation and maintenance optimization module. According to the invention, the directional acoustic sensor collects voiceprint signals of key components, synchronously collects multi-dimensional operation parameters, and combines a wavelet threshold noise reduction algorithm to filter environmental noise and timestamp synchronization algorithm associated data, thereby achieving the effects of eliminating environmental interference and ensuring the integrity and accuracy of monitoring data. A normal voiceprint baseline library is constructed through historical voiceprint data and a deep learning model, current voiceprint signals are compared in real time, the operation state is judged in combination with multi-dimensional parameters, a time sequence model is used for deducing the fault trend, and the effects of accurately recognizing the fault type, accurately dividing the equipment state and conducting early warning in advance are achieved.
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