Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Jun Lin, Lei Su, Yingjie Yan, Gehao Sheng, Da Xie and Xiuchen Jiang
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Jun Lin: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Lei Su: Electric Power Research Institute of State Grid Shanghai Municipal Electric Power Company, Shanghai 200120, China
Yingjie Yan: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Gehao Sheng: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Da Xie: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Xiuchen Jiang: Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2018, vol. 11, issue 7, 1-14

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.

Keywords: dissolved gas analysis; long short-term memory; deep belief network; running state prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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