Advanced Transformer Fault Prediction via LSTM and Digital Twin Integration|Chapter 8 | Engineering Research: Perspectives on Recent Advances Vol. 9

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

An Applied Study of the Impact of Augmented Reality on Museum Experience: A Focus on Museums in Namibia | Chapter 9 | Research Highlights in Science and Technology Vol. 8

 The study illustrates Augmented Reality usage for reimaging the place for viewing artifacts or experience in Namibia. A special focus act the National Museum of Namibia as a reference point and framework to implement the proposed improved reality solution. The purpose concerning this proposal search out demonstrate how Augmented Reality can embellish the museum knowledge by providing complimentary able to be seen with eyes information to the exhibits. This is to increase the place for viewing artifacts or visiting interest, especially with young people. An connected to the internet survey was done to understand the society's personal knowledge when visiting a museum the traditional hole or door in vessel order to understand what betterings they would like to see in museums for a better occurrence. 64 participants took part in the study. This survey hold up implementing a operating system-based resolution, focusing on the target consumers. Our results showed that Augmented Reality improves the museum experience, information, and learning, in addition to an emotional network of the museum visitor when utilizing the augmented phenomenon mobile use, compared to the traditional place for viewing artifacts or experience. From an growth perspective, this study emphasized the need to acknowledge implementing a related solution on additional mobile app platforms, as iOS was secondhand as the benchmark for this study.

Author(s) Details:

Tatenda Audrey Chanakira,
Department of Computing, Mathematics and Statistical Sciences, University of Namibia, Namibia.

Ari Happonen,
Software Engineering Department, LUT University, Finland.

Victoria Hasheela,
Department of Computing, Mathematics and Statistical Sciences, University of Namibia, Namibia.

Please see the link here: https://stm.bookpi.org/RHST-V8/article/view/11545