Dr Arvind Jain

About

Dr. Arvind Kumar Jain received B.E. from Govt. Engineering College (GEC) Jabalpur, and M. Tech. and Ph.D. degrees in electrical engineering from Indian Institute of Technology Kanpur, India. He is currently working as an Associate Professor with the Electrical Engineering Department, National Institute of Technology (NIT), Agartala (Tripura), India. Before joining NIT Agartala, he rendered his services at Rustam JI Institute of Technology (An Institute of Border security Force) as a Faculty of Electrical Engineering, HOD-EE, Dean Academics, and Officiating Principal(for 2.5 years).
Dr Jain conferred with POSOCO Power System Award-2014 for doctoral research work. This award is jointly given by Power System Operation and Control Corporation (an ancillary of Power grid Corporation India Limited) and Indian Institute of Technology Delhi. He is also received Madhya Pradesh Council of Science and Technology (MPCST) and SRIJAN award for academic excellence and Newton-Bhabha fellowship of British Council and SERB-DST.
Dr. Jain published about 35 research papers in International/National journals/conferences, and 03-book chapters. He also published 01-book on “Demand Response in Modern Power Grid”.His current research interests include power systems restructuring, Demand Response, Electric mobility, AI and IOT applications to Smart grid.

Employment

• National Institute of Technology Agartala Professor
  2019 - Present

Education

• Indian Institute of Technology Kanpur Ph D
  2007 - 2013

Projects & Funding

Projects & funding information is unavailable.

Publications (29)

• Real time validation of masked auto encoder enabled computer vision approach for classification of complex power quality disturbances
  Electric Power Systems Research 2027 DOI: 10.1016/j.epsr.2026.113558
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• Design of an LSTM and Bi-LSTM-driven intelligent protection scheme for standalone low-voltage DC microgrid
  Journal of Renewable and Sustainable Energy 2025 DOI: 10.1063/5.0290701
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• An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid
  Electrical Engineering 2024 DOI: 10.1007/s00202-024-02334-7
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• Optimal design and development of a microgrid for off-grid rural communities
  International Journal of Information Technology 2024 DOI: 10.1007/s41870-023-01708-7
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• Assessment of Transient Disturbance using Discrete Fourier Transform and Feed Forward Neural Network based Hybrid Classifier
  2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET) 2023 DOI: 10.1109/sefet57834.2023.10245639
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• Voltage Harmonics and Transient Disturbances Detection and Classification using GoogLeNet Model of Deep Learning
  2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET) 2023 DOI: 10.1109/sefet57834.2023.10245322
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• Development of Demand Response Mechanism to Reduce the Impact of EV Charging on Power Distribution Transformers
  Tuijin Jishu/Journal of Propulsion Technology 2023
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• Integrated Energy Systems: Design, Control and Operation
  Nova Science Publishers, Inc.USA 2023 DOI: 10.52305/slpi5681
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• Machine Learning Applications in Smart Grid
  Energy Systems in Electrical Engineering 2023 DOI: 10.1007/978-981-99-2800-2_9
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• Optimal Demand Response Strategy of an Industrial Customer
  Industrial Demand Response: Methods, best practices, case studies, and applications, IET UK 2022 DOI: 10.1049/pbpo215e
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