Qualification: 
Ph.D, M.Tech
j_ramprabhakar@blr.amrita.edu

Dr. J. Ramprabhakar currently serves as  Assistant Professor (S G) at the department of Electrical & Electronics, Amrita School of Engineering, Bangalore campus. He completed his Ph.D.  from Indian Institute of Technology, Gandhinagar. He has 7 years of Teaching Experience, 3 years of Industrial Experience and 4.5 years of Research Experience.

Education

DEGREE/PROGRAM INSTITUTION
PhD Indian Institute of Technology Gandhinagar
M.Tech (PE) Amrita Vishwa Vidyapeetham
B.Tech (ICE) Adhiyaman college of Engineering, Hosur, (Madras University)

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2017

Conference Paper

K. Sivakumar, Dr. J. Ramprabhakar, and Dr. Shankar S., “Coordination of wind-hydro energy conversion system with sliding mode control”, in 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016, 2017.[Abstract]


This paper proposes a sliding mode control strategy for achieving the reliable operation of wind-hydro energy conversion system in isolated mode. With this control strategy it is viable to generate electrical power in localities with favorable meteorological condition and limited grid connectivity. In this work, a self-excited induction generator is used which is driven by the variable wind speed turbine and another self-excited induction generator which is driven by hydro turbine assuming constant water flow. The simulation results are displayed to show the effectiveness of the control strategy to maintain voltage at the point of common coupling and also its frequency constant. © 2016 IEEE.

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2016

Conference Paper

B. M. Prabhakar, Dr. J. Ramprabhakar, and Sailaja, V., “Estimation and controlling the state of charge in battery augmented photovoltaic system”, in 2016 - Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy, PESTSE 2016, 2016.[Abstract]


Today solar technology is proving reliable and increasingly affordable. It is good to environment and a secure energy supply. The overall carbon footprint for generating solar electricity is 30 times less than using coal. The main drawback in PV system is the fluctuation in solar energy supply. In this work Kalman filter is used to estimate the State of Charge in battery storage system, and hence it is possible to know about the duration for which the demand can be met. The SOC estimation technique using Kalman filter which is an accurate adaptive method and by using three switches the charge of battery is maintained within the safe limit (20%-80%) so that overcharging and over discharging can be eliminated and hence the battery life and performance can also be improved. The efficacy of the proposed method is verified by a set of simulation using MATLAB simulink. © 2016 IEEE. More »»

2016

Conference Paper

K. Kiranvishnu, Dr. J. Ramprabhakar, and K. Sireesha, “Comparative study of wind speed forecasting techniques”, in 2016 - Biennial International Conference on Power and Energy Systems: Towards Sustainable Energy, PESTSE 2016, 2016.[Abstract]


Wind is one of the prominent renewable source, as it is clean energy and abundantly available, however there are immense issues owing to varying nature of wind flow. The said issues has attracted many researchers to work pertinent to wind forecasting models and hence it is possible for wind power forecast based on the capacity of wind turbine based energy conversion system. With the devised forecasting techniques, it is also possible to schedule required demand to match the generation. Forecasting is an important aid in wind speed prediction. The wind speed forecasting can effectively reduce or avoid the adverse effect of wind farm on power grid and it will also ensure the safe and economic operations of the power system. This work is aimed to design an appropriate model for wind forecasting with less mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square error (MSE) in comparison to back propagation neural network (BPNN) and linear regression technique. The simulation of the mentioned and proposed models are done and the results are presented to validate the effectiveness of the proposed technique

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AMRITA
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