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Prediction Method for Power Transformer Running State Based on LSTM_DBN Network

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jun Lin & Lei Su & Yingjie Yan & Gehao Sheng & Da Xie & Xiuchen Jiang, 2018. "Prediction Method for Power Transformer Running State Based on LSTM_DBN Network," Energies, MDPI, vol. 11(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1880-:d:158732
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    References listed on IDEAS

    as
    1. Hui Song & Jiejie Dai & Lingen Luo & Gehao Sheng & Xiuchen Jiang, 2018. "Power Transformer Operating State Prediction Method Based on an LSTM Network," Energies, MDPI, vol. 11(4), pages 1-15, April.
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    Cited by:

    1. Kai Ding & Chen Yao & Yifan Li & Qinglong Hao & Yaqiong Lv & Zengrui Huang, 2022. "A Review on Fault Diagnosis Technology of Key Components in Cold Ironing System," Sustainability, MDPI, vol. 14(10), pages 1-28, May.
    2. Hong Pan & Chenyang Hang & Fang Feng & Yuan Zheng & Fang Li, 2022. "Improved Neural Network Algorithm Based Flow Characteristic Curve Fitting for Hydraulic Turbines," Sustainability, MDPI, vol. 14(17), pages 1-15, August.
    3. Jiang, Wuhao & Wang, Kai & Lv, Yan & Guo, Jianfeng & Ni, Zhongjin & Ni, Yihua, 2020. "Time series based behavior pattern quantification analysis and prediction — A study on animal behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    4. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
    5. Bing Zeng & Jiang Guo & Fangqing Zhang & Wenqiang Zhu & Zhihuai Xiao & Sixu Huang & Peng Fan, 2020. "Prediction Model for Dissolved Gas Concentration in Transformer Oil Based on Modified Grey Wolf Optimizer and LSSVM with Grey Relational Analysis and Empirical Mode Decomposition," Energies, MDPI, vol. 13(2), pages 1-20, January.

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