Method for predicting concentration of dissolved gas in transformer oil based on PSO-LSTM

Abstract

The invention discloses a method for predicting the concentration of dissolved gas in transformer oil based on PSO-LSTM. The method achieves the effective evaluation of the operation state of a transformer through the accurate prediction of the concentration of the dissolved gas in the transformer oil. The method comprises the steps of firstly, collecting online oil chromatography sample data of atransformer, determining state characteristic parameters of the data, performing normalization processing, and dividing a training set and a test set; secondly, constructing a long-term and short-term memory network prediction model, optimizing the long-term and short-term memory network prediction model through a particle swarm algorithm to obtain two optimal prediction model parameters, and reestablishing a long-term and short-term memory network model according to the obtained optimal prediction model parameters; and finally, by taking the concentrations of seven characteristic gases dissolved in the oil as inputs and taking the concentration of the gas to be predicted as an output, predicting the concentration of the dissolved gas in the transformer oil. The method provided by the invention can accurately predict the change of the concentration of the dissolved gas in the transformer oil, can provide a certain theoretical basis for fault diagnosis and operation condition evaluation of the transformer, and provides a reference for operation and maintenance personnel to overhaul.

Description

technical field [0001] The invention relates to the technical field of power equipment monitoring, in particular to a method for predicting the concentration of dissolved gas in transformer oil. Background technique [0002] As the most core equipment in the power system, the power transformer plays an important role in the distribution and transmission of electric energy. It is an important asset of the power grid company. Its safe and stable operation is the prerequisite for ensuring the reliable power supply of the power grid. Under normal operation of the transformer, due to the aging of the internal insulating solid, a small amount of gas will be dissolved in the insulating oil, mainly hydrogen (H 2 ), methane (CH 4 ), ethane (C 2 h 6 ), ethylene (C 2 h 4 ), acetylene (C 2 h 2 ), carbon monoxide (CO), carbon dioxide (CO 2 ) and other gases. Different operating conditions of the transformer can be judged according to the concentration ratio of the dissolved gas ...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a PSO-LSTM method for predicting the concentration of dissolved gas in transformer oil, which solves the problems of insufficient model fitting ability and low prediction accuracy caused by selecting parameters based on experience. It can quickly search and determine the optimal parameters of the long-short-term memory network model, and the training efficiency is high, thereby improving the prediction accuracy and providing theoretical guidance for subsequent fault diagnosis and state evaluation of transformers

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