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Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks

Author

Listed:
  • Aniket Vatsa

    (Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

  • Ananda Shankar Hati

    (Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad 826004, India)

  • Vadim Bolshev

    (Laboratory of Power Supply and Heat Supply, Federal Scientific Agroengineering Centre VIM, 109428 Moscow, Russia)

  • Alexander Vinogradov

    (Laboratory of Power Supply and Heat Supply, Federal Scientific Agroengineering Centre VIM, 109428 Moscow, Russia)

  • Vladimir Panchenko

    (Department of Theoretical and Applied Mechanics, Russian University of Transport, 127994 Moscow, Russia)

  • Prasun Chakrabarti

    (Department of Computer Science and Engineering, ITM SLS Baroda University, Vadodara 391510, India)

Abstract

Power transformers play a crucial role in maintaining the stability and reliability of energy systems. Accurate moisture assessment of transformer oil-paper insulation is critical for ensuring safe operating conditions and power transformers’ longevity in large interconnected electrical grids. The moisture can be predicted and quantified by extracting moisture-sensitive dielectric feature parameters. This article suggests a deep learning technique for transformer moisture diagnostics based on long short-term memory (LSTM) networks. The proposed method was tested using a dataset of transformer oil moisture readings, and the analysis revealed that the LSTM network performed well in diagnosing oil insulation moisture. The method’s performance was assessed using various metrics, such as R-squared, mean absolute error, mean squared error, root mean squared error, and mean signed difference. The performance of the proposed model was also compared with linear regression and random forest (RF) models to evaluate its effectiveness. It was determined that the proposed method outperformed traditional methods in terms of accuracy and efficiency. This investigation demonstrates the potential of a deep learning approach for identifying transformer oil insulation moisture with a R 2 value of 0.899, thus providing a valuable tool for power system operators to monitor and manage the integrity of their transformer fleet.

Suggested Citation

  • Aniket Vatsa & Ananda Shankar Hati & Vadim Bolshev & Alexander Vinogradov & Vladimir Panchenko & Prasun Chakrabarti, 2023. "Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks," Energies, MDPI, vol. 16(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2382-:d:1085337
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    References listed on IDEAS

    as
    1. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    2. Pawel Zukowski & Przemyslaw Rogalski & Tomasz N. Kołtunowicz & Konrad Kierczynski & Marek Zenker & Alexander D. Pogrebnjak & Matej Kucera, 2022. "DC and AC Tests of Moisture Electrical Pressboard Impregnated with Mineral Oil or Synthetic Ester—Determination of Water Status in Power Transformer Insulation," Energies, MDPI, vol. 15(8), pages 1-16, April.
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    5. Inmaculada Fernández, 2022. "The Need for Experimental and Numerical Analyses of Thermal Ageing in Power Transformers," Energies, MDPI, vol. 15(17), pages 1-4, September.
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