Transformers are the heart of electric power systems, and their operational state decides whether or not the power network is well-regulated. Electrical, mechanical, and thermal stresses cause some gases created during an operation to dissolve in insulating oil. The most significant tool for defect diagnostics in transformers is dissolved gas analysis (DGA). The time series prediction of dissolved gas levels in oil, when combined with dissolved gas analysis, provides a foundation for transformer fault diagnosis and an early warning. A long short-term memory (LSTM) based prediction model is developed in this paper to train the digital twin for identifying the essential fault in the transformer via DGA. The model is fed with three different gas concentrations as input. This study achieves the performance evaluation in terms of validation accuracy. The suggested model exhibits significant validation accuracy of 99.83%, as indicated by the analyses, thus aiding the early prediction of transformer maintenance. It can be validated that the LSTM model for fault identification and analysis using dissolved gas in the transformer has a lot of research potential. The study concluded that the trained digital twin integrated with the test transformer's condition monitoring system can precisely envisage the transformer's useful life. Its application in transformer online monitoring using a mobile device can be investigated.
Author(s)
Details
GVSSN
Srirama Sarma
Department of Electrical and Electronics Engineering, Matrusri
Engineering College, Hyderabad, India.
Bumanapalli
Ravindranath Reddy
Deputy Executive Engineer, Jawaharlal Nehru Technological University Hyderabad
(JNTUH University), Hyderabad, India.
Pradeep
Nirgude
Ultra High Voltage Research Laboratory (UHVRL), Central Power
Research Institute (CPRI), Hyderabad, India.
Please see the book here:- https://doi.org/10.9734/bpi/erpra/v9/4142
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