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Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM

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Listed:
  • Zhao Luo
  • Zhiyuan Zhang
  • Xu Yan
  • Jinghui Qin
  • Zhendong Zhu
  • Hao Wang
  • Zeyong Gao

Abstract

Dissolved gas analysis (DGA) is the most important tool for fault diagnosis in electric power transformers. To improve accuracy of diagnosis, this paper proposed a new model (SDAE-LSTM) to identify the dissolved gases in the insulating oil of power transformers and perform parameter analysis. The performance evaluation is attained by the case studies in terms of recognition accuracy, precision ratio, and recall ratio. Experiment results show that the SDAE-LSTM model performs better than other models under different input conditions. As evidenced from the analyses, the proposed model achieves considerable results of recognition accuracy (95.86%), precision ratio (95.79%), and recall ratio (97.51%). It can be confirmed that the SDAE-LSTM model using the dissolved gas in the power transformer for fault diagnosis and analysis has great research prospect.

Suggested Citation

  • Zhao Luo & Zhiyuan Zhang & Xu Yan & Jinghui Qin & Zhendong Zhu & Hao Wang & Zeyong Gao, 2020. "Dissolved Gas Analysis of Insulating Oil in Electric Power Transformers: A Case Study Using SDAE-LSTM," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, November.
    • Handle: RePEc:hin:jnlmpe:2420456
    DOI: 10.1155/2020/2420456
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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. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.

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