A Transformer Fault Diagnosis Method Based on Deep Forest Model

Abstract

The invention discloses a transformer fault diagnosis method based on a deep forest model. Firstly, the uncoded ratio of dissolved gas analysis data in transformer oil is used as a characteristic parameter of the deep forest model, and then the sample data is divided into a training set and a test set; and then The deep forest model DF is constructed. The deep forest model DF extracts more characteristic information from the multi-dimensional data of transformer faults through multi-granularity scanning, and then trains through cascading forests to achieve the best effect of diagnosing and identifying transformer fault types. This method effectively improves the accuracy of transformer fault diagnosis and provides a reliable basis for operation and maintenance personnel to correctly judge the operating status of the transformer.

Description

technical field [0001] The invention relates to the technical field of power equipment monitoring, in particular to a transformer fault diagnosis method based on a deep forest model. Background technique [0002] With the development of energy interconnection, transformer failure will endanger the safe and stable operation of the entire power system. Therefore, to quickly and accurately understand the fault type of the transformer, so as to carry out maintenance work, can provide an important guarantee for the normal operation of the power system. [0003] Dissolved Gas Analysis (DGA) is mainly used in the online monitoring of oil-immersed transformers. Based on DGA characteristic gas for data correlation analysis, researchers at home and abroad have proposed three-ratio method, Rogers ratio method, Dornenburg ratio method, electric co-research method and other analysis methods, but the traditional DGA method only gives the threshold discrimination boundary of fault diagnos...

AI Technical Summary

Problems solved by technology

For example, the expert system cannot learn independently, the work efficiency is low, and it is difficult to obtain accurate diagnosis results; the neural network has strong self-learning ability, but requires a large amount of sample data for training, hyperparameter adjustment is complicated and the learning cycle is long, and it is easy to fall into a local minimum ;The decision tree relies on optimizing the local optimum to achieve the overall optimum, and it is difficult to ensure that it returns to the global optimum, and it is easy to overfit. The above diagnostic methods all have the problem that it is difficult to effectively deal with high-dimensional data and feature information extraction.

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