Transformer partial discharge detection method and system based on multi-modal feature combination

Through the combination of multimodal feature combination and neural network model, the problem of signal propagation and positioning deviation in local discharge detection of transformers is solved, efficient and accurate detection is achieved, and the false judgment rate and false alarm probability are reduced.

CN119881565AInactive Publication Date: 2025-04-25QINGDAO LIDONG ELECTRONICS CO LTD
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Patent Information

Application Number
CN202510386337.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-31
Publication Date
2025-04-25
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

In the prior art, the local discharge detection method for transformers has problems such as signal propagation affected by the shell structure, positioning deviation and electromagnetic interference. The calculation resource consumption is high and the probability of false alarms is high when running in parallel.

Method used

Using a detection method based on the joint multimodal feature, the multi-dimensional feature fusion of electromagnetic wave signals, pulse current signals and acoustic wave signals is used to realize cross-modal correlation learning, eliminate information islands, and realize adaptive signal fusion through dynamic weight coefficients.

Benefits of technology

Effectively reduce the misjudgment rate, improve the signal-to-noise ratio, reduce the data processing volume, improve detection efficiency and accuracy, especially in oil paper insulation discharge scenarios.

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Abstract

The invention discloses a transformer partial discharge detection method and system based on multi-modal feature combination, and relates to the technical field of transformer detection, and the key points of the technical scheme are that the method comprises the steps: collecting an electromagnetic wave signal, a pulse current signal and a sound wave signal of a target transformer; respectively extracting an electromagnetic wave time-frequency matrix, a pulse current waveform parameter and a sound wave space coordinate; performing multi-modal feature fusion on the electromagnetic wave time-frequency matrix, the pulse current waveform parameters and the sound wave space coordinates based on a dynamic weighting strategy to generate a joint feature tensor; and inputting the joint feature tensor into a pre-constructed neural network model, identifying a discharge type and optimizing a positioning coordinate. According to the method, multi-modal features are subjected to tensor fusion, cross-modal correlation learning is realized through the neural network model, information islands can be eliminated, and the misjudgment rate is effectively reduced; and electromagnetic wave-current-sound wave signal adaptive fusion is realized through a dynamic weight coefficient, and the signal-to-noise ratio is obviously improved through characteristic complementation in an oil paper insulation discharge scene.
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