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

Dimitris A. Barkas, Stavros D. Kaminaris, Konstantinos K. Kalkanis, George Ch. Ioannidis and Constantinos S. Psomopoulos ()
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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

Energies, 2022, vol. 16, issue 1, 1-17

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.

Keywords: Dissolved Gas Analysis; neural networks; adaptive algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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