A Transformer Fault Diagnosis Method Considering the Operating State Level

Abstract

The invention discloses a fault diagnosis method for a transformer taking into account the level of operating status. The steps include: collecting 5 typical fault data of transformers, constructing a fault diagnosis sample data set, and carrying out label processing; extracting 7 gas contents and 19 gas ratios from transformer oil chromatographic data as fault features, and preprocessing 26 Dimensional fault features use principal component analysis (PCA) for feature reduction and fusion; construct an adaptive correlation vector machine fault diagnosis model based on particle swarm optimization and K-fold cross-validation; when the transformer's operating status level is evaluated as a serious fault, Diagnose using a fault diagnosis model. The fault diagnosis model constructed by the present invention realizes automatic optimization of kernel parameters during the model training process, and is simple in structure compared with multi-level binary classifiers. The risk of misdiagnosis in real-time online fault diagnosis of traditional transformers can be reduced by taking into account the fault diagnosis of the operating status level.

Description

technical field [0001] The invention relates to the technical field of transformer fault diagnosis, in particular to a transformer fault diagnosis method which takes into account the operating state level. Background technique [0002] As one of the key pivotal equipment in the power system, the transformer runs uninterruptedly in the power system, and its own operating state directly affects the safe and stable operation of the entire power system. When the transformer is in a serious operating condition and effective measures are not taken in time, it may cause fire, explosion, and even lead to large-scale power outages. Therefore, when the transformer fails, it is of great significance to accurately judge the fault type of the transformer through fault diagnosis technology, which can facilitate the staff to take corresponding maintenance measures in time, reduce the fault loss and avoid the expansion of the accident. [0003] Due to the high reliability of transformer op...

AI Technical Summary

Problems solved by technology

Obviously, this diagnosis method does not meet the actual requirements of transformer real-time online fault diagnosis, and the imbalance between normal and fault data may lead to serious misdiagnosis and missed diagnosis.

In addition, the traditional fault diagnosis method directly divides the transformer into normal and fault types. The standard of this classification method is too rough and ignores the sub-health state (general defect) of the transformer, which has great ambiguity and uncertainty.

When the transformer has general defects, it is difficult to obtain effective diagnosis results by traditional diagnosis methods, which brings safety hazards to the reliable operation of the transformer

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