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730 results on '"Rathore, Akshay"'

9. Analysis and Design of Extended Range Zero Voltage Switching (ZVS) Active-Clamping Current-Fed Push–Pull Converter

Author

Khatun, Koyelia, Rathore, Akshay Kumar, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Saini, H. S., editor, Srinivas, T., editor, Vinod Kumar, D. M., editor, and Chandragupta Mauryan, K. S., editor

Published
2020
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11. Enhancing Transformer Health Monitoring With AI-Driven Prognostic Diagnosis Trends: Overcoming Traditional Methodology’s Computational Limitations

Author

Vatsa, Aniket, Hati, Ananda Shankar, and Rathore, Akshay Kumar

Abstract

Power transformers are critical for grids, facilitating transmission, distribution, and voltage conversion. However, its insulation degrades over time, instigating catastrophic failures. This article investigates the emerging trends of artificial intelligence (AI) driven transformer prognostic health management (TPHM) strategies to mitigate such risks, emphasizing AI’s role in overcoming traditional methodology’s computational limitations. Hybrid approaches enhance dissolved gas analysis accuracy, while frequency response analysis is interpreted using machine learning. Deep-learning automates feature-weight assessment in partial-discharge and insulation analysis, eliminating manual health-index formulation. The implications and strategies for optimizing AI-driven TPHM efficiency are discussed, providing valuable trends in fostering a prognostic diagnosis ecosystem.

Published
2024
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21. Effect of compaction and moisture content on the deformation characteristics of unsaturated sandy soils varying fines content.

Author

Chowdepalli, Bhargavi, Singh Rathore, Akshay, and Watanabe, Kenji

Abstract

This research explores how the dynamic behaviour under cyclic compression loading affects compaction characteristics. Three types of sand, varying in fines content, are compacted under different saturation (${{\rm{S}}_{\rm{r}}}$ S r ) and compaction (${{\rm{D}}_{\rm{c}}}$ D c ) levels. Suction-controlled drained cyclic loading tests are conducted to predict axial strain accumulation, with initial suction measured for each compacted state. The study seeks to understand the correlation between axial strain accumulation and initial suction across soil types. Findings indicate that initial soil suction, influenced by saturation, significantly impacts axial strain under cyclic loading. Lower saturation enhances particle bonding, increasing initial suction and reducing strain accumulation. Moreover, an increase in fines content results in greater strain accumulation. Despite that, sands with elevated fines (18.8%) can achieve similar soil performance through effective compaction with precise saturation control. This underscores the role of saturation in moisture regulation during compaction, particularly for sands with higher non-plastic fines content, in achieving effective compaction. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Guest Editorial: Special Issue on Machine Learning Techniques in Power Electronics

Author

Rathore, Akshay Kumar, Doolla, Suryanarayana, and Monti, Antonello

Abstract

Power conditioning systems are performing a key role in smart grid operation. The increase in renewable energy installation leads to an increase in the power electronic converters installation for distributed generation, rural electrification, and grid integration. As the numbers are increasing, the reliability of the grid is impacted mainly due to the power conditioning weak link. Thus, there is a need to improve the reliability and efficiency of overall power processing. One way to achieve this is to improve health by smart monitoring and make the controller fault-tolerant. For example, approaches that monitor the health of the devices along with fault-tolerant control architecture can be implemented in the controller to detect failures and to get timely alarm signals. For this, relevant data gathering and analysis are extremely critical. Machine learning (ML) and deep learning (DL) are approaches that analyze the data, learn from the data, and then apply it during the decision-making process. The Special Issue on Machine Learning Techniques in Power Electronics invites the articles related to data gathering/analysis and improvements of reliable operation of power conditioning and renewable energy resources with applications to the smart grid.

Published
2023
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