Transformer fault feature fusion diagnosis method and system based on multi-modal time series data reconstruction

By using a multimodal time-series data reconstruction method, combined with oil chromatography, vibration and infrared thermal imaging data, a multi-physics field evolution trajectory of the transformer is generated and fault feature fusion diagnosis is performed. This solves the problem of insufficient diagnosis under single monitoring methods and achieves highly accurate and timely fault diagnosis and maintenance decision-making.

CN121980473BActive Publication Date: 2026-06-26四川盐源华电新能源有限公司 +2
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Patent Information

Application Number
CN202610434353.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-04-03
Publication Date
2026-06-26
Estimated Expiration
2046-04-03

AI Technical Summary

Technical Problem

Existing transformer fault diagnosis methods mainly rely on single monitoring means, which cannot fully reflect the electrical and mechanical faults inside the transformer, and lack effective fusion and in-depth mining of multimodal time series data, resulting in insufficient diagnostic accuracy and timeliness.

Method used

By acquiring multimodal time-series data captured by an oil chromatography monitor, vibration acceleration sensor, and infrared thermal imager, and using a transformer operation status reconstruction network for cross-modal physical field spatiotemporal alignment processing, a multi-physical field evolution trajectory of the transformer is generated. Combined with topological source tracing of fault feature clues and semantic parsing of fault modes, a comprehensive diagnostic descriptor is generated to achieve fusion diagnosis of fault type and spatial location.

Benefits of technology

This improved the accuracy and reliability of transformer fault diagnosis, achieved an organic combination of fault diagnosis and maintenance decision-making, ensured the safe and stable operation of transformers, and improved the reliability and stability of the power system.

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Abstract

The application provides a transformer fault feature fusion diagnosis method and system based on multi-modal time series data reconstruction, and relates to the technical field of power system transformer fault diagnosis. First, the time series waveforms of dissolved gas in oil, the time series waveforms of box wall vibration and the time series waveforms of sleeve heat map are captured respectively, and are input into a pre-constructed transformer operation situation reconstruction network for cross-modal physical field space-time alignment processing, to generate a transformer multi-physical field evolution track with a fault energy injection point as a starting reference. Topological source tracking processing of fault feature clues is performed on the track, to obtain a transformer global fault feature topology chain. The chain is input into a fault mode semantic analysis network, to generate a transformer comprehensive diagnosis descriptor that fuses fault type semantic labels and fault space positioning coordinates. According to the descriptor, a local evolution track segment is extracted, a maintenance decision trigger instruction containing a fault residual life consumption rate is generated, and is sent to a transformer online monitoring master station system, to ensure safe and stable operation of the transformer.
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