Journal papers

1. Vikas Singh, Tukaram Moger and Debashisha Jena, “Uncertainty handling techniques in power systems: A critical review”, Electric Power Systems Research, Volume 203, 107633, February 2022, pp: 1-2, Published online October 2021. DOI:https://doi.org/10.1016/j.epsr.2021.107633, SCI

2. Reddiprasad Reddivari,Debashisha Jena, “A Correlative Investigation of Impedance Source Networks: A Comprehensive Review”, IETE Technical Review, Jan 2021, pp.1-34, doi.10.1080/02564602.2020.1870006 (Yes)

3. T. N. Gautam,R. Reddivari,and D. Jena, “A cost-effective single-phase semi flipped gamma type magnetically coupled impedance source inverters”, International Journal of Circuit Theory and Applications,  Vol. 49, Issue. 4, Apr 2021, pp. 1078-1102, doi.org/10.1002/cta.2865 (Yes)

4. Ranjan,K. G.,Tripathy,D. S.,Prusty,B. R.,& Jena,D, “ An improved sliding window prediction based outlier detection and correction for volatile time series”, International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, Vol. 34, Issue. 1, Feb. 2021,  pp. 1-13, doi.org/10.1002/jnm.2816 (Yes)

5. Gaurav Ranjan, B Rajanarayan Prusty, Debashisha Jena, “Review of preprocessing methods for univariate volatile time series in power system applications”, Electric Power Systems Research, Vol. 191, Feb. 2021, pp. 1-17, doi.org/10.1016/j.epsr.2020.106885, (Yes)

6. Reddiprasad Reddivari,Debashisha Jena,T. N. Gautam, “Analysis, Design, and Performance Evaluation of DifferentialMode Y-Source Converters for Voltage Spikes Mitigation”, IEEE Transactions on Industry Applications, Vol. 56, Nov 2020, pp. 6701-6710, doi.10.1109/TIA.2020.3019228(Yes)

7.  Kancharana Vinod Kumar,Reddiprasad Reddivari & Debashisha Jena.  “A Comparative Study of Different Capacitor Voltage Control Design Strategies for Z-Source Inverter”                 IETE Journal of Research  Aug,2019          pp.1-11.https://doi.org/10.1080/03772063.2019.1650669. Yes

8.  Vivekananda Subburaj, Debashisha Jena&Parthiban Perumal.       “Two phase (reconfigurable) inverting switched capacitor converter for micro power applications and its accurate equivalent resistance calculation”, IEEE Transactions on Circuits and Systems II: Express Briefs         66(8)    Aug, 2019 pp. 1446-1450https://doi.org/10.1109/TCSII.2018.2886076

9.  Reddivari, R., & Jena, D. (2019). “A Negative Embedded Differential Mode Γ-Source Inverter with Reduced Switching Spikes”. IEEE Transactions on Circuits and Systems II: Express Briefs. (early access) https://doi.org/10.1109/TCSII.2019.2941597

10. Reddivari, R., & Jena, D. (2019). Analysis of RCD Snubber Based Non-Ideal Z-source Inverter Using Average Modelling Approaches. International Journal of Electronics, (just-accepted). https://doi.org/10.1080/00207217.2019.1672811

11. Subburaj, V., Zhaikhan, A., Jena, D., Parthiban, P., Mustafa, Y., Ruderman, A. “Investigation of a Family of Dual-Output Coupled/Decoupled Switched Capacitor Converter for Low Power Applications” IET Circuits, Devices & Systems. (2018).

12. Prusty, B. R., Jena, D. “A spatiotemporal probabilistic model‐based temperature‐augmented probabilistic load flow considering PV generations” International Transactions on Electrical Energy Systems, e2819. (2019)

13. Subburaj, V., Jena, D., Perumal, P., Mahnashi, Y. “High Efficiency Two-phase Switched-capacitor Converter with Seven Distinct Negative Voltage Ratios for Power Saving Applications” International Journal of Electronics Letters, (just-accepted).

14. 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

15. 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

16. 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

17. 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

18. Prusty, B. R., & Jena, D. (2016). Combined cumulant and Gaussian mixture approximation for correlated probabilistic load flow studies: a new approach. 1.    CSEE Journal of Power and Energy Systems, 2(2), 71-78.

19. Ramana, V. V., Jena, D., & Gaonkar, D. N. (2016). An Accurate Modeling of Different Types of Photovoltaic Modules Using Experimental Data. 1.    International Journal of Renewable Energy Research (IJRER), 6(3), 970-974. www.ijrer.org/ijrer/index.php/ijrer/article/download/4010/pdf

20. 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

21. 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

22. Saravanakumar, R., & Jena, D. (2016). Control Strategy to Maximize Power Extraction in Wind Turbine. Distributed Generation & Alternative Energy Journal, 31(4), 27-49

23. Rajendran, S., & Jena, D. (2015). Load Mitigation and Optimal Power Capture for Variable Speed Wind Turbine in Region 2. Journal of Renewable Energy

24.  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.2015.01.03

25.   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

26. 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

27. 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

28.  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

29.  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

30. 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

31. 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

32. 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

33. 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 

34. 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

35. 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

36. 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

37. 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. Vikas Singh, Tukaram Moger and Debashisha  Jena, “Modified Cumulant based Probabilistic Load Flow Considering Correction between Loads and Wind Power Generations, 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET) to be held virtually in Dubai during 20 to 21 May 2022.

2. Vikas Singh, Tukaram Moger and Debashisha  Jena, “Probabilistic Load Flow Considering Load and Wind Power Uncertainties using Modified Point Estimation Method”, 2022  Third International Conference on Smart Grid and Renewable Energy (SGRE-2022) virtually held during 20 to 22 March 2022 and jointly organized by Texas A & M University, Qatar, and Smart Grid Centre (SGC). 

3. Singh, V., Moger, T., Jena, D, “ Comparative Evaluation of Basic Probabilistic Load Flow Methods with Wind Power Integration”, 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, ICEPE 2020, 5-7 March 2021, Meghalaya, doi.10.1109/ICEPE50861.2021.9404524

4. Kumbale,S.,Pius,J.,Reddivari,R.,Jena,D, “Component level reliability evaluation of boost converter, Z-Source, and improved gamma type ysource inverters”,  2020 IEEE International Conference on Power Systems Technology, POWERCON 2020, 14-16 September  2020, Bangalore, doi.10.1109/POWERCON48463.2020.9230563. 

5. Tripathy, D.S., Prusty, B.R., Jena, D, “Probabilistic Forecasting of Daily PV Generation Using Quantile Regression Method”,Proceedings - 2020 IEEE India Council International Subsections Conference, INDISCON 2020, 3-4 October 2020, Visakhapatnam, doi.10.1109/INDISCON50162.2020.00060.

6. Tripathy, D.S., Prusty, B.R., Jena, D, “Short-term PV generation forecasting using quantile regression averaging”, 2020 IEEE International Conference on Power Systems Technology, POWERCON 2020, 14-16 September  2020, Bangalore, doi.10.1109/POWERCON48463.2020.9230535.

7. Tripathy, D.S., Prusty, B.R., Jena, D,Tripathy, D.S., Rajanarayan Prusty, B., Jena, D., Sahu, M.K, “ Multi-time instant probabilistic PV generation forecasting using quantile regression forests”, PIICON 2020 - 9th IEEE Power India International Conference, 20th Feb- ist Mar 2020, Delhi, doi.10.1109/PIICON49524.2020.9112880.

8. Goutham, T. N., Reddivari, R., &Jena, D, “Design Implementation of High Boost Embedded Semi Quasi-ZSI for Photovoltaic System Applications”, IEEE Global Conference for Advancement in Technology (GCAT) (pp. 1-6), Nagarjuna College, Bangalore, October,2019.

9. Gowrishankar, K., Jena, D., & Reddivari, R, “Comparative Overview of Proportional-Integral, State Feedback Integral, and Sliding Mode Controllers for Buck Converter’, In 2019 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) (pp. 1-6), MIT, Manipal, August,2019.

10. Reddivari, R., Jena, D. “A critical analysis of Z-source converters considering the effects of internal resistances” International Journal of Electronics, 105(10), 1785-1803. (2018).

11. Subburaj, V., Mustafa, Y., Zhaikhan, A., Jena, D., Perumal, P., Ruderman, A. “Two phase (reconfigurable) inverting switched capacitor converter for micro power applications and its accurate equivalent resistance calculation.” IEEE Transactions on Circuits and Systems II: Express Briefs. (2018).

12. Jena, D., Reddivari, R. (2019). “A Novel Active Clamped Y-Source Network for Improved Voltage Boosting”. IET Power Electronics. (2019).

13. Reddiprasad Reddivari and Debashisha Jena “Differential mode Y-Source DC to Dc Converter for better performance with Loosely coupled inductors ” IEEE PEDES 2018 at IIT, Madras during 17 to 21 December, 2018.

14. Zhaikhan, A., Subburaj, V., Mustafa, Y., Jena, D., Perumal, P., & Ruderman, an “An Algorithm Steps to Solve Coupled Case for Dual Input Dual Output SCC.” TENCON 2018-2018 IEEE Region 10 Conference (pp. 0821-0825). IEEE.

15. Reddiprasad, R., Jena, D., Goutham. “Differential Mode Y-source DC-DC Converter for Better Performance with Loosely Coupled Inductors.” In PEDES-2018 IEEE (pp. 1-6). IEEE

16. 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). 

17. 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).

18. 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.

19.  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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. 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.

28. Vijay, M., Jena, D., (2014). GA Based Adaptive Controller for 2DOF Robot Manipulator. IFAC Proceedings Volumes, 47(1), 670-675.

29. 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.

30. 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.

31. 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.

32. 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.

33. 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.

34. 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.

35. 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.

36. 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.

37. 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.

38. 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.

39. 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.

40. 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.

Book Chapter

1. Vikas Singh, Tukaram Moger and Debashisha  Jena, “Probabilistic Steady-State Analysis of Power Systems Integrated with Renewable Generations”, Book chapter in Renewable Energy Integration to Grid: A Probabilistic Perspective published by CRC Press, Taylor and Francis Group (www.taylorandfrancis.com), pp. 199-238,  March 2022 (DOI: https://doi.org/10.1201/9781003271857). 

2.  B. Rajanarayan Prusty and Debashisha Jena  “Uncertainty Modeling Steps for Probabilistic Steady-State Analysis”  Lecture Notes in Electrical Engineering 553       June, 2019  pp. 1169-1177               https://link.springer.com/chapter/10.1007/978-981-13-6772-4_102.

3. B. Rajanarayan Prusty and Debashisha Jena  “Probabilistic Load Flow in a Transmission System Integrated with Photovoltaic Generations”  Lecture Notes in Electrical Engineering  June, 2019        pp.1159-1168  https://link.springer.com/chapter/10.1007/978-981-13-6772-4_101.

4. Prusty, B. R., and Jena, D, "Probabilistic load flow in a transmission system integrated with photovoltaic generations" is published in Applications of Computing, Automation and Wireless Systems in Electrical Engineering, Springer. pp. 1-7.

5. Prusty, B. R., and Jena, D, "Uncertainty modeling steps for probabilistic steady state analysis" published in Applications of Computing, Automation and Wireless Systems in Electrical Engineering, Springer, pp.1-11.