Transformer DGA data prediction method based on multi-dimensional time sequence frame convolution LSTM

Abstract

The invention discloses a transformer DGA data prediction method based on multi-dimensional time sequence frame convolution LSTM. The transformer DGA data prediction method includes the steps: firstly, collecting and dividing monitoring information of dissolved gas in transformer substation oil into a test set and a verification set; secondly, further extracting characteristic parameters by adopting a non-coding ratio method, deleting data which are basically kept unchanged, and performing normalization, noise superposition and the like; performing windowing transformation on the processed data set to form a time sequence frame; constructing a C-LSTM network, and inputting the time sequence frame data into a network convolution layer to obtain a time sequence characteristic quantity; training the C-LSTM network through the training set and the verification set, performing a prediction effect test by using the verification set, and continuously optimizing network parameters; and settinga network updating period, and continuously updating the to-be-predicted transformer in a later monitoring task. According to the transformer DGA data prediction method, the convolutional LSTM network is introduced into transformer fault prediction, and the DGA data ratio characteristics are fully extracted, and the complex association characteristics of the multi-dimensional time sequence are considered, so that relatively accurate prediction is realized.

Description

technical field [0001] The invention relates to a power transformer fault prediction method, in particular to a method for predicting dissolved gas data in transformer oil based on temporal frame convolution extraction features and LSTM deep learning framework for training and modeling. Background technique [0002] Power transformers play a vital role in the power system and are 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. Dissolved gas analysis (DGA) in oil can fully reflect the operation and maintenance information of transformers, and comprehensively use artificial intelligence, big data and other advanced technologies to analyze the trend of DGA monitoring data of power transformers, which is a major research hotspot to guide transformer health management. [0003] Tra...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide an intelligent prediction method for the analysis data of dissolved gas in transformer oil, improve the prediction accuracy, and solve the problems of traditional methods that are difficult to deal with data correlation and massive data

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