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Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning

Author

Listed:
  • Dimitris A. Barkas

    (Department of Electrical and Electronics Engineering, University of West Attica, GR-12244 Egaleo, Greece)

  • Stavros D. Kaminaris

    (Department of Electrical and Electronics Engineering, University of West Attica, GR-12244 Egaleo, Greece)

  • Konstantinos K. Kalkanis

    (Department of Electrical and Electronics Engineering, University of West Attica, GR-12244 Egaleo, Greece)

  • George Ch. Ioannidis

    (Department of Electrical and Electronics Engineering, University of West Attica, GR-12244 Egaleo, Greece)

  • Constantinos S. Psomopoulos

    (Department of Electrical and Electronics Engineering, University of West Attica, GR-12244 Egaleo, Greece)

Abstract

Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network.

Suggested Citation

  • Dimitris A. Barkas & Stavros D. Kaminaris & Konstantinos K. Kalkanis & George Ch. Ioannidis & Constantinos S. Psomopoulos, 2022. "Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning," Energies, MDPI, vol. 16(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:54-:d:1009875
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    References listed on IDEAS

    as
    1. Ancuța-Mihaela Aciu & Claudiu-Ionel Nicola & Marcel Nicola & Maria-Cristina Nițu, 2021. "Complementary Analysis for DGA Based on Duval Methods and Furan Compounds Using Artificial Neural Networks," Energies, MDPI, vol. 14(3), pages 1-22, January.
    2. Petar Sarajcev & Antonijo Kunac & Goran Petrovic & Marin Despalatovic, 2022. "Artificial Intelligence Techniques for Power System Transient Stability Assessment," Energies, MDPI, vol. 15(2), pages 1-21, January.
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    Cited by:

    1. Georgi Ivanov & Anelia Spasova & Valentin Mateev & Iliana Marinova, 2023. "Applied Complex Diagnostics and Monitoring of Special Power Transformers," Energies, MDPI, vol. 16(5), pages 1-24, February.

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