Research Theme
Smart Computing Framework (High Performance Computing, Artificial Intelligence (AI), Machine Learning (ML), Big Data Analytics, Optimization) Applied to Smart Grid.
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Consumer loads are changing instantaneously.
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The nature of the grid is hence very dynamic.
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We have to supply power at the minimal cost to the consumers.
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Renewables are abundantly available, and the cost per unit is very low and is also green.
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Renewable Share has to be increased in the grid.
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But as the renewable share increases, there arises lots of instability problems in the grid. (Voltage and frequency).
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Also, enduser comfort should not be compromised.
So, the question finally arises
HOW MUCH MAXIMUM RENEWABLE SHARE WE CAN ALLOW AT ANY TIME?
WHAT ARE THE METHEDOLOGIES AND TECHNIQUES FOR MAXIMIZING RENEWABLE ENERGY PENETRATION?
How Artificial Intelligence Methods adds value to the the above Mission
Academic Awards
Best Research Paper Award
ICETEM Conference, 2014
Best Paper Award
NCPSE National Conference, 2011
International Conferences
Author of over 57 papers presented at leading global venues like IEEE GM, PESGRE, and ICUE Thailand, establishing new benchmarks in international grid research.
57+
International Conference Papers Published Worldwide
International Study Reports
USAID consultancy report on “Desk study on technical gaps of country - specific grid codes and regulations and recommendations for a common Asian wide-grid code” USAID clean power Asia, June 2021. Contract No.: AID-486-C-16-00001 Book
Current Ph.D. Students
Dr. Harikrishnan G. R.
(Completed 2026 April)
Big Data Analytics For Smart Energy Management in Smart Cities.
NIKHIL BINOY C
Optimal Control Strategy For Energy Management in Smart Grid
Nesihath M K
Renewable energy integrated residential energy management system for community micro grid
