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Open AccessArticle

Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

1
Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
2
Electric Power Research Institute of State Grid Shanghai Municipal Electric Power Company, Shanghai 200120, China
*
Author to whom correspondence should be addressed.
Energies 2018, 11(7), 1880; https://doi.org/10.3390/en11071880
Received: 3 July 2018 / Revised: 9 July 2018 / Accepted: 12 July 2018 / Published: 19 July 2018
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. View Full-Text
Keywords: dissolved gas analysis; long short-term memory; deep belief network; running state prediction dissolved gas analysis; long short-term memory; deep belief network; running state prediction
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MDPI and ACS Style

Lin, J.; Su, L.; Yan, Y.; Sheng, G.; Xie, D.; Jiang, X. Prediction Method for Power Transformer Running State Based on LSTM_DBN Network. Energies 2018, 11, 1880.

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