Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Jun Lin,Lei Su,Xiuchen Jiang,Yingjie Yan,Gehao Sheng,Da Xie

doi:10.3390/en11071880

Jun Lin, Lei Su + Show 4 more

Open Access

https://doi.org/10.3390/en11071880

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Journal: Energies	Publication Date: Jul 19, 2018
Citations: 28	License type: CC BY 4.0

Affiliation: Shanghai Jiao Tong University, Shanghai Electric (China)

Abstract

It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly, based on the trend of gas concentration in transformer oil, a long short-term memory (LSTM) model is established to predict the future characteristic gas concentration. Then, the accuracy and influencing factors of the LSTM model are analyzed with examples. The deep belief network (DBN) model is used to establish the transformer operation using the information in the transformer fault case library. The accuracy of state classification is higher than the support vector machine (SVM) and back-propagation neural network (BPNN). Finally, combined with the actual transformer data collected from the State Grid Corporation of China, the LSTM_DBN model is used to predict the transformer state. The results show that the method has higher prediction accuracy and can analyze potential faults.

Highlights

As one of the important pieces of equipment in the power system, power transformers can directly influence the stability and safety of the entire power grid
If the transformer fails in operation, it will cause power to turn off and cause damage to the transformer itself and the power system, which may result in greater damage [1]
This paper presents a prediction method of transformer running state based on LSTM_DBN

Summary

Introduction

As one of the important pieces of equipment in the power system, power transformers can directly influence the stability and safety of the entire power grid. The LSTM model is used to process the superiority of the time series, and the gas concentration in the future is predicted based on the trend of the gas concentration in the transformer oil history. The ratio of the future gas concentration obtained from the LSTM prediction model is used as the DBN network input to classify the future operating status of the transformer. The ability of LSTM model to deal with time series is used to analyze the changing trend of dissolved gas concentration data in transformer oil to obtain the future gas concentration and calculate the gas concentration ratio. The entire LSTM_DBN model makes full use of the historical data of the transformer oil chromatogram and realizes the analysis of the state of the transformer in the future and the analysis of the early fault warning. Through the analysis of specific examples, we can see that the model proposed in this paper has good prediction accuracy and can analyze potential faults

Prediction of Dissolved

Principles of Prediction

Transformer

Structure

Results

Running State Prediction

Conclusions