Transformer fault diagnosis method based on Bi-LSTM and analysis of dissolved gas in oil

Abstract

The invention discloses a transformer fault diagnosis method based on Bi-LSTM and analysis of dissolved gas in oil. The method comprises: collecting fault DGA monitoring data of each substation, carrying out normalization, sequence expansion, noise superimposing and the like on the data, and extracting fault feature information based on a non-coding ratio method; carrying out length ranking on a DGA sequence, carrying out grouping and filling, and classifying groups into a training set and a verification set; constructing a deep learning frame based on Bi-LSTM, inputting data, and carrying outtraining; and then carrying out diagnosis and network updating by combining actual test data to obtain a fault diagnosis model with the high diagnosis accuracy and portability. According to the invention, the influence of the noise and error on the diagnosis during the DGA data monitoring process is reduced effectively; and the Bi-LSTM-based transformer fault diagnosis model is constructed by considering the complex correlation between different sequences. With introduction of links of sequence sorting, grouping, filling and the like, a problem of different sampling lengths of different transformers in the actual engineering is solved by using the batch training strategy.

Description

technical field [0001] The invention belongs to the technical field of power transformer fault diagnosis, in particular to a transformer fault diagnosis method based on Bi-LSTM and analysis of dissolved gas in oil. Background technique [0002] The power transformer is the key equipment in the power system and the basis for the economical, safe and stable operation of the power system. With the gradual advancement of Industry 4.0 and the ubiquitous power Internet of Things, the online monitoring data of power transformers shows an explosive growth trend. Therefore, comprehensively using advanced technologies such as artificial intelligence and big data to perform fault diagnosis and state prediction on the online monitoring data of large-capacity power transformers is a major research hotspot in guiding the related work of transformer operation and maintenance. [0003] Dissolved gas analysis (DGA) in oil can fully reflect the transformer fault status. Traditional DGA-base...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a new diagnostic method for the analysis of dissolved gas in transformer oil, which is more intelligent and has higher accuracy, and solves the problems of traditional methods such as too absolute encoding, wrong data and wrong diagnosis.

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