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

Review of Data Mining Techniques in Environmental System: An Advanced Approach | Chapter 9 | Recent Advances in Mathematical Research and Computer Science Vol. 1

Medical imaging, network traffic analysis, environmental systems, and other sectors have benefited from the development of diverse data mining methods. The environment system is now the most essential topic of concern for individuals in today's society, as it has a daily impact on human lives. ES elements such as earthquakes, soil erosion, deforestation, rising summer temperatures, rain fall density/intensity, flood occurrences, and the most significant is the impact of all of these ES factors on human people and their behaviour, both directly and indirectly. Data mining methods can be used to uncover patterns in data that is widely spread, heterogeneous, sparse, multidimensional, and heterogeneous, such as data from the Environment System. This study provides a brief overview of the key phases, techniques, and processes involved in developing and dealing with ES data, which are critical in the development of a data mining tool for detecting and understanding patterns in environmental system data sets. The data mining techniques used in the design of ES Tool span from processing crude data sets to translating them into patterns for examination.

 

Author (S) Details

M. S. Chaudhari
Department of Computer Science & Engineering, Priyadarshini Bhagwati College of Engineering, RTM Nagpur University, Nagpur, Maharashtra, India.

N. K. Choudhari
Priyadarshini Bhagwati College of Engineering, RTM Nagpur University, Nagpur, Maharashtra, India.

View Book:- https://stm.bookpi.org/RAMRCS-V1/article/view/4339

A Survey of Research on Handwritten and Printed Kannada Numeral Recognition Technique | Chapter 3 | Advanced Aspects of Engineering Research Vol. 5

Technology is progressing at a breakneck pace, and digital recognitions are playing an increasingly important role, opening up new opportunities for OCR study. In comparison to English and other western numerals, Kannada handwritten numerals are more difficult to recognise. Many researchers, on the other hand, have proposed a real-time solution for printed Kannada numerals. Researchers are still faced with numerous motivating hurdles when it comes to recognising printed number texts. Current research offers a variety of strategies for recognising Kannada handwritten papers with moderate accuracy and for recognising handwritten numerals. In each phase of the recognition process, different approaches have been used, however each approach only provides a solution for a small number of numeral sets.

Author(s) Details

Vishweshwarayya C. Hallur
KLES BK College, Chikodi, Karnataka, India.

View Book :- https://stm.bookpi.org/AAER-V5/article/view/983

Brain Tumor Image Segmentation Using Ant Colony Optimization Technique | Chapter 15 | Emerging Trends in Engineering Research and Technology Vol. 9

 

This chapter deals with Pre-Processing and Enhancement methods of Medical images. The proposed
method consists of four processing stages. In first stage, the MRI (Magnetic Resonance Imaging)
Brain Image is acquired from MRI Brain Image data set. In second stage, MRI Image is given to the
Pre-Processing stage, where the film artifacts (labels) are removed. In third stage, the high frequency
components are removed from MRI Image using various filtering techniques. This work investigates
the most effective optimization method, known as Ant Colony Optimization (ACO), introduced in the
field of Medical Image Processing.

Author(s) Details

Dr. M. Duraisamy
Thiruvalluvar University College of Arts and Science, Tirupattur – 635901, Tamil Nadu, India.

Dr. T. Logeswari
New Horizon College, Banglore, Karnataka, India.

View Book :- https://bp.bookpi.org/index.php/bpi/catalog/book/235