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Power Transformer Operating State Prediction Method Based on an LSTM Network

Author

Listed:
  • Hui Song

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Jiejie Dai

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Lingen Luo

    (Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Gehao Sheng

    (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

The state of transformer equipment is usually manifested through a variety of information. The characteristic information will change with different types of equipment defects/faults, location, severity, and other factors. For transformer operating state prediction and fault warning, the key influencing factors of the transformer panorama information are analyzed. The degree of relative deterioration is used to characterize the deterioration of the transformer state. The membership relationship between the relative deterioration degree of each indicator and the transformer state is obtained through fuzzy processing. Through the long short-term memory (LSTM) network, the evolution of the transformer status is extracted, and a data-driven state prediction model is constructed to realize preliminary warning of a potential fault of the equipment. Through the LSTM network, the quantitative index and qualitative index are organically combined in order to perceive the corresponding relationship between the characteristic parameters and the operating state of the transformer. The results of different time-scale prediction cases show that the proposed method can effectively predict the operation status of power transformers and accurately reflect their status.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:914-:d:140840
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    Citations

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    Cited by:

    1. Mohammed G. Ragab & Said J. Abdulkadir & Norshakirah Aziz & Qasem Al-Tashi & Yousif Alyousifi & Hitham Alhussian & Alawi Alqushaibi, 2020. "A Novel One-Dimensional CNN with Exponential Adaptive Gradients for Air Pollution Index Prediction," Sustainability, MDPI, vol. 12(23), pages 1-22, December.
    2. Yiyang Sun & Xiangwen Wang & Junjie Yang, 2022. "Modified Particle Swarm Optimization with Attention-Based LSTM for Wind Power Prediction," Energies, MDPI, vol. 15(12), pages 1-17, June.
    3. Mst. Shapna Akter & Hossain Shahriar & Reaz Chowdhury & M. R. C. Mahdy, 2022. "Forecasting the Risk Factor of Frontier Markets: A Novel Stacking Ensemble of Neural Network Approach," Future Internet, MDPI, vol. 14(9), pages 1-23, August.
    4. Chao Liu & Ailin Zhang & Junhua Xue & Chen Lei & Xiangzhen Zeng, 2023. "LSTM-Pearson Gas Concentration Prediction Model Feature Selection and Its Applications," Energies, MDPI, vol. 16(5), pages 1-16, February.
    5. 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.

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