Debashisha Jena

Designation: 

Associate Professor

Date of Joining at NITK: 

Wednesday, April 22, 2009

Professional Experience: 

11 years

Contact Details

E-mail: 

bapu4002[AT]gmail[DOT]com

Telephone: 

+91-824-2473466

Mobile: 

+91-9019148131

Personal Website: 

https://sites.google.com/site/djena4002
Faculty (author) Identifiers

ORCID iD: 

0000-0001-8800-4652

Scopus Author ID: 

55389939300
Academic Background
  • Ph.D (NIT Rourkela, Orissa) 2010
  • M.Tech (BPUT, Orissa) 2004
  • B.E (UCE Burla, Orissa) 1996
Areas of Interest
  • System identification and control using soft computing techniques.
  • Application to power systems, robotic manipulators and renewable energy systems such as wind, solar and fuel cell system.
  • Evolutionary optimisation algorithms and its application to static and dynamic neural network.
Significant Projects

1. Principal Investigator (PI), FPGA based development of different MPPT algorithms for a stand-alone photo voltaic system using artificial intelligence. (25.07 lakhs Ministry of Power, CPRI), Completed
2. Co-PI, Investigation on the operation and control of multiple distribute generator sources in microgrid. (Phase-II), (25 lakhs Ministry of Power, CPRI) (ongoing)
3. Principal Investigator (PI), “Adaptive MPPT of Grid- tied Photovoltaic System using Magnetically Coupled Impedance Source Inverters” (DST SERB, EMR project (SERB/EMR/2016/005851), 24.36 Lakhs) (Ongoing)

Supervision of Ph.D
  • Completed - 01
  • Ongoing - 05
Significant Publications

Journal papers

  1. Prusty, B. R., & Jena, D. (2016). A sensitivity matrix based temperature augmented probabilistic load flow. IEEE Transaction on Industry Applications. ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=4957013
  2. Prusty, B. R., & Jena, D. (2016). A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach. Renewable and Sustainable Energy Reviews. 69, 1286-13022.   http://dx.doi.org/10.1016/j.rser.2016.12.044.
  3. Vijay, M., & Jena, D. (2016). PSO based neuro fuzzy sliding mode control for a robot manipulator. Journal of Electrical Systems and Information Technology.   http://dx.doi.org/10.1016/j.jesit.2016.08.006
  4. Vijay, M., & Jena, D. (2016). Intelligent adaptive observer-based optimal control of overhead transmission line de-icing robot manipulator. Advanced Robotics, 30(17-18), 1215-1227. http://dx.doi.org/10.1080/01691864.2016.1207562
  5. Prusty, B. R., & Jena, D. (2016). Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach. CSEE Journal of Power and Energy Systems, 2(2), 71-78.
  6. Ramana, V. V., Jena, D., & Gaonkar, D. N. (2016). An Accurate Modeling of Different Types of Photovoltaic Modules Using Experimental Data. International Journal of Renewable Energy Research (IJRER), 6(3), 970-974. www.ijrer.org/ijrer/index.php/ijrer/article/download/4010/pdf
  7.  Saravanakumar, R., & Jena, D. (2016). Nonlinear control of wind turbine with optimal power capture and load mitigation. Energy Systems, 7(3), 429-448.       http://link.springer.com/article/10.1007/s12667-015-0170-8
  8.  Saravanakumar, R., & Jena, D. (2016). Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction. International Journal of Green Energy, 13(3), 309-319. http://dx.doi.org/10.1080/15435075.2014.952424
  9. Saravanakumar, R., & Jena, D. (2016). Control Strategy to Maximize Power Extraction in Wind Turbine. Distributed Generation & Alternative Energy Journal, 31(4), 27-49.
  10. Rajendran, S., & Jena, D. (2015). Load Mitigation and Optimal Power Capture for Variable Speed Wind Turbine in Region 2. Journal of Renewable Energy, 2015.
  11. Saravanakumar, R., & Jena, D. (2015). Validation of an integral sliding mode control for optimal control of a three blade variable speed variable pitch wind turbine. International Journal of Electrical Power & Energy Systems, 69, 421-429. http://dx.doi.org/10.1016/j.ijepes.2015.01.031
  12. Jena, D., & Rajendran, S. (2015). A review of estimation of effective wind speed based control of wind turbines. Renewable and Sustainable Energy Reviews, 43, 1046-1062. http://dx.doi.org/10.1016/j.rser.2014.11.088
  13. Rajendran, S., & Debashisha, J. E. N. A. (2015). Backstepping sliding mode control of a variable speed wind turbine for power optimization. Journal of Modern Power Systems and Clean Energy, 3(3), 402-410. link.springer.com/article/10.1007/s40565-015-0106-2
  14. Jena, D., & Ramana, V. V. (2015). An accurate modeling of photovoltaic system for uniform and non-uniform irradiance. International Journal of Renewable Energy Research (IJRER), 5(1), 29-40. www.ijrer.org/ijrer/index.php/ijrer/article/view/1732
  15. Jena, D., & Ramana, V. V. (2015). Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review. Renewable and Sustainable Energy Reviews, 52, 400-417.  http://dx.doi.org/10.1016/j.rser.2015.07.079
  16. Rajendran, S., & Jena, D. (2014). Variable speed wind turbine for maximum power capture using adaptive fuzzy integral sliding mode control. Journal of Modern Power Systems and Clean Energy, 2(2), 114-125. link.springer.com/article/10.1007/s40565-014-0061-3
  17. Saravanakumar R, Jena Debashisha (2014) Adaptive fuzzy sliding mode control for variable speed wind turbine for maximum power capture. WSEAS Trans Power Syst. 9, 281-290. www.wseas.org/multimedia/journals/power/2014/a125716-207.pdf
  18. Vijay, M.; Jena, D. (2012), “A Comparison of PI Tuning by Direct Search and Genetic Algorithm for Optimal Control of AGC in Continuous-Discrete Mode,” The Journal of CPRI, 2012
  19. Subudhi, B., & Jena, D. (2011). A differential evolution based neural network approach to nonlinear system identification. Applied Soft Computing, 11(1), 861-871. www.sciencedirect.com/science/article/pii/S1568494610000116
  20. Subudhi, B., & Jena, D. (2011). Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing, 74(10), 1696-1709. www.sciencedirect.com/science/article/pii/S0925231211001056 
  21. Subudhi, B., & Jena, D. (2009). An improved differential evolution trained neural network scheme for nonlinear system identification. International Journal of Automation and Computing, 6(2), 137-144. http://link.springer.com/article/10.1007/s11633-009-0137-0
  22. Subudhi, B., & Jena, D. (2009). Nonlinear system identification using opposition based learning differential evolution and neural network techniques.IEEE Journal of Intelligent Cybernetic Systems, 1, 1-13.
  23. Subudhi, B., & Jena, D. (2009). Differential evolution computation applied to parameter estimation of induction motor. Archives of Control Sciences, 19 (1), 5-26. acs.polsl.pl/index.php?mode=2&show=39
  24. Subudhi, B., & Jena, D. (2008). Differential evolution and Levenberg Marquardt trained neural network scheme for nonlinear system identification. Neural Processing Letters, 27(3), 285-296. link.springer.com/article/10.1007/s11063-008-9077-x

 

Referred Conferences

  1. B. R. Prusty and D. Jena, “An Efficient Hybrid Technique for Correlated Probabilistic Load Flow Study with Photovoltaic Generations,” Power Systems Conference (NPSC), 2016 Nineteenth National, Bhubaneswar, 2016, pp. 1-6 (Accepted for publication). 
  2. N. G. Bhat, B. R. Prusty and D. Jena,“Modeling of Power Demands of Electric Vehicles in Correlated Probabilistic Load Flow Studies,”Power Electronics, Drives and Energy Systems (PEDES), 2016 IEEE International Conference on, Trivandrum, 2016, pp. 1-6.(Accepted for publication).
  3. Prusty, B. Rajanarayan, and Debashisha Jena. "Estimation of optimal number of components in Gaussian mixture model-based probabilistic load flow study." In India Conference (INDICON), 2016 IEEE Annual, pp. 1-6. IEEE, 2016.
  4.  Jena, D. (2016, August). Mesh analysis by direct matrix manipulation a different approach. In Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), IEEE (pp. 173-178). IEEE.
  5. Prusty, B. R., & Jena, D. (2015, December). Modeling of correlated photovoltaic generations and load demands in probabilistic load flow. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.
  6. Prusty, B. R., & Jena, D. (2015). Sequence operation theory based probabilistic load flow assessment with photovoltaic generation. Proceedings of Michael Faraday IET International Summit: MFIIS(pp.28-36). IET.
  7. Rampelli, M., & Jena, D. (2015). Advantage of Unscented Kalman Filter Over Extended Kalman Filter in Dynamic State Estimation of Power System Network. Proceedings of Michael Faraday IET International Summit: MFIIS(pp. 48-56). IET.
  8. Ramana, V. V., & Jena, D. (2015, February). Maximum power point tracking of PV array under non-uniform irradiance using artificial neural network. In Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015 IEEE International Conference on (pp. 1-5). IEEE.
  9. Vijay, M., & Jena, D. (2015, February). Optimal backstepping sliding mode control for robot manipulator. In Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2015 IEEE International Conference on (pp. 1-5). IEEE.
  10. Swain, P., & Jena, D. (2015, March). Modeling, simulation & optimal control of non-linear PEM fuel cell with disturbance input. In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on (pp. 1-7). IEEE.
  11. Swain, P., & Jena, D. (2015, March). PID control design for the pressure regulation of PEM fuel cell. In Recent Developments in Control, Automation and Power Engineering (RDCAPE), 2015 International Conference on (pp. 286-291). IEEE.
  12. Rajendran, S., & Jena, D. (2015, May). Adaptive nonsingular terminal sliding mode control for variable speed wind turbine. In Electrical and Computer Engineering (CCECE), 2015 IEEE 28th Canadian Conference on (pp. 937-942). IEEE.
  13. Vijay, M., Jena, D., (2014). GA Based Adaptive Controller for 2DOF Robot Manipulator. IFAC Proceedings Volumes, 47(1), 670-675.
  14. Saravanakumar, R., & Jena, D. (2014, January). ISMC based variable speed wind turbine for maximum power capture. In Control, Instrumentation, Energy and Communication (CIEC), 2014 International Conference on (pp. 326-330). IEEE.
  15. Rajendran, S., & Jena, D. (2014, December). Backstepping Sliding Mode Control for variable speed wind turbine. In 2014 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.
  16. Jena, D., & Ramana, V. V. (2013, December). Simple and accurate method of modeling Photovoltaic module: A different approach. In Green Computing, Communication and Conservation of Energy (ICGCE), 2013 International Conference on (pp. 465-469). IEEE.
  17. Saravanakumar, R., & Jena, D. (2013, December). Second order ISMC for variable speed wind turbine. In 2013 IEEE 8th International Conference on Industrial and Information Systems (pp. 594-598). IEEE.
  18. Saravanakumar, R., & Jena, D. (2013, January). Nonlinear estimation and control of wind turbine. In Electronics, Computing and Communication Technologies (CONECCT), 2013 IEEE International Conference on (pp. 1-6). IEEE.
  19. Vijay, M., & Jena, D. (2012, February). A continuous-discrete mode of optimal control of AGC for multi area hydrothermal system using genetic algorithm. In 2012 International Conference on Computing, Communication and Applications (pp. 1-6). IEEE.
  20. Song, K. Y., Gupta, M. M., Jena, D., & Subudhi, B. (2009, June). Design of a robust neuro-controller for complex dynamic systems. In Fuzzy Information Processing Society, 2009. NAFIPS 2009. Annual Meeting of the North American (pp. 1-5). IEEE.
  21. Song, K. Y., Gupta, M. M., & Jena, D. (2009, October). Design of an error-based robust adaptive controller. In Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on (pp. 2386-2390). IEEE.
  22. Subudhi, B., & Jena, D. (2009, January). Nonlinear system identification of a twin rotor MIMO system. In TENCON 2009-2009 IEEE Region 10 Conference (pp. 1-6). IEEE.
  23. Subudhi, B., Jena, D., & Gupta, M. M. (2008, December). Memetic differential evolution trained neural networks for nonlinear system identification. In 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems (pp. 1-6). IEEE.
  24. Subudhi, B., & Jena, D. (2008, November). A combined differential evolution and neural network approach to nonlinear system identification. In TENCON 2008-2008 IEEE Region 10 Conference (pp. 1-6). IEEE.
  25. Subudhi, B., Kumar, A. A., & Jena, D. (2008, November). dSPACE implementation of fuzzy logic based vector control of induction motor. In TENCON 2008-2008 IEEE Region 10 Conference (pp. 1-6). IEEE.
Achievements
  • GSEP scholarship by the Government of Canada for doing research in the University of Saskatchewan Canada.

Books Published

  1. Authored a book on“ Basic Electrical Engineering” By Dr. Debashisha Jena Wiley India Publication  ISBN:978-81-265-3613-9 (Single author)
  2. Authored a book on“Power System Analysis Operation and Control” By Debashisha Jena and B. Rajnarayana Prusty, I. K.  International Publishing House Pvt. Ltd. ISBN 978-93-82332-49-7 (Double Author)
  3. B. Subudhi, D. Jena, Evolutionary Computing Approaches to System Identification, Handbook of Research on Computational Intelligence Applications in Bioinformatics, (IGI Global, 2016)
 

Contact us

Shubhanga K N

Professor and Head of the Department

Department of Electrical and Electronics Engineering
National Institute of Technology Karnataka, Surathkal
Srinivasnagar, Surathkal, Mangalore-575025. Karnataka, India.
Ph : +91-824-2473045
Fax: +91-824-2474039
E-mail: ee_hod[AT]nitk[DOT]edu[DOT]in

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