Ashish Kumar
Vidya Vihar Institute of Technology, Purnia, Government Polytechnic Khagaria, National Institute of Technology Jamshedpur, National Institute of Technical Teachers Training and Research
0000-0001-6883-7868
About
ASHISH KUMAR (Member, IEEE) received a master’s degree from the Department of Electrical Engineering, NITTTR, Chandigarh, India. He is currently pursuing a PhD degree with the Electrical Engineering Department, National Institute of Technology, Jamshedpur, India. He has been awarded a full Higher Degree Research Scholarship to pursue her doctorate. Previously, He worked as an Assistant Professor with the Electrical Engineering Department, at VVIT, Purnia, Bihar, India. His areas of research are power systems optimization, cyber security, cyber-physical Systems, machine learning, deep learning and smart power grids.
Employment
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Vidya Vihar Institute of Technology, Purnia Assistant Professor2022 - 2023
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Government Polytechnic Khagaria Guest Lecturer2021 - 2022
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Netaji Subhas institute of technology patna Assistant Professor2014 - 2021
Education
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National Institute of Technology Jamshedpur Ph.D. Scholar2023 - 2028
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National Institute of Technical Teachers Training and Research Masters in Engineering2016 - 2022
Projects & Funding
Projects & funding information is unavailable.
Publications (9)
- An adaptive residual informed LSTM-based cybersecurity framework for resilient DC microgrid operation under false data injection attacks Save
- A Multi-Class Electricity Theft Classification Framework Using CatBoost for Residential Consumption Profiles Save
- Data Driven ML forecasting model for Energy Management of EV Charging Station Save
- COA-Based Optimization of THD and Dc-Link Voltage of Half-Bridge Compensator for Electric Traction Save
- Electricity Theft Detection using Ensemble-Based Machine Learning Classifier Save
- Optimal Energy Management in Coordination With Virtual Power Plant Save
- Half Bridge Compensator Design for THD Reduction and DC Link Voltage Stabilisation of Electric Traction Save
- An Ensemble Supervised Machine Learning Model for Solar Irradiance Prediction Using Tree-Based Learners Save
- A Comprehensive Perspective of Different Compensators of Railway Traction System Save