A transformer fault diagnosis method based on small sample unbalanced data set

By constructing a transformer fault diagnosis model based on a small-sample unbalanced dataset, and utilizing a diversified hierarchical bootstrap resampling and expert system-enhanced Bagging-stacking fusion mechanism, the problems of complex fault mode recognition and accurate diagnosis of specific categories in transformer fault diagnosis are solved, achieving high-precision fault identification and stable operation of the power system.

CN121412830BActive Publication Date: 2026-06-09SHENYANG AGRI UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Patents(China)
Current Assignee / Owner
SHENYANG AGRI UNIV
Filing Date
2025-10-17
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Existing technologies for transformer fault diagnosis in the context of small-sample imbalanced datasets suffer from problems such as complex fault mode recognition, inaccurate diagnosis of specific categories, and insufficient model generalization ability. This results in the inability to accurately diagnose transformer faults, threatening the safe and stable operation of the power system.

Method used

A transformer fault diagnosis model based on a small sample imbalanced dataset is constructed. By acquiring dissolved gas analysis data of transformers, and combining diversified hierarchical Bootstrap resampling, fast feature selection, base learner combination, category expert system and probabilistic fusion logic module, an integrated model is built for diagnosis.

Benefits of technology

It improves the predictive accuracy and model generalization ability of transformer fault diagnosis, enhances the level of intelligent transformation of the power system, and ensures the safe and stable operation of the power grid.

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Abstract

The application discloses a transformer fault diagnosis method based on a small sample unbalanced data set, comprising the following steps: S1, acquiring a transformer dissolved gas analysis data set and a comprehensive feature set; S2, constructing a transformer fault diagnosis model based on a small sample unbalanced data set; S3, according to the gas analysis data set and the comprehensive feature set, performing model training on the constructed transformer fault diagnosis model to obtain an optimal fault diagnosis model; and realizing transformer fault diagnosis based on the small sample unbalanced data set according to the optimal fault diagnosis model. The application solves the problems that the current traditional method cannot accurately diagnose transformer faults due to the technical problems such as complex fault mode recognition, specific class accurate diagnosis, insufficient model generalization ability and difficulty in fusion parameter optimization under a small sample unbalanced fault data set in the prior art.
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Citation Information

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