Dr. Krishnama Raju Suraparaju

Brief Bio

Brief Bio
Dr. Krishnama Raju is an Assistant Professor in the Department of Electrical Engineering, with a specialization in Control Systems. He holds both M.Tech and Ph.D. degrees in Control Systems, and has a strong passion for applying advanced control and machine learning techniques to modern power and energy systems.
His current research focuses on the application of Machine Learning and Reinforcement Learning to Power Electronic Converters and Microgrids, aiming to develop intelligent and adaptive control strategies for real-world energy systems. He has also worked extensively on fuzzy logic-based control for renewable energy systems, particularly in Wind energy integration.
He is keen to mentor motivated undergraduate and postgraduate students interested in control systems, power electronics, and AI-based energy solutions. His research group actively explores innovative ideas that bridge theory with practical implementation, using hardware-in-the-loop (HIL) systems.
If you’re enthusiastic about shaping the future of smart and sustainable energy through advanced control and learning algorithms, you are welcome to join his research group.

Education

DegreeYearSpecializationUniversity
AMIE(UG)2005Electrical EngineeringInstitution of Engineers(India)
M.Tech2011Control systems EngineeringIIT Kharagpur
Ph.D.2016Electrical EngineeringIIT Roorkee
PostDoc2019Hybrid Electric VehiclesUniversity of Warwick, UK

Contact Details

Phone:Email:Address:
(+91-712-280-2228)krs@eee.vnit.ac.in; skrajuvnit@gmail.com211, New Academic Building, VNIT Nagpur

Research Interest

Research Interest
1. Application of Artificial Intelligence to Electrical Systems especially Power Electronics and Microgrid Control
2. Control Systems
Sr. No.TitleFunding AgencyRoleYearAmount (in Lakhs INR)
DescriptionAuthor(s)PublisherISBNYear
Krishnama Raju, S. (2023). Application of Type-2 Fuzzy Logic Controllers in Renewable Energy Systems. In: Castillo, O., Kumar, A. (eds) Recent Trends on Type-2 Fuzzy Logic Systems: Theory, Methodology and Applications. Studies in Fuzziness and Soft Computing, vol 425.Krishnama Raju, S.,Castillo, O., Kumar, A. Springer, ChamOnline ISBN978-3-031-26332-3; Print ISBN978-3-031-26331-62023
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S.No.Paper Citation
1L. Goyal, P. Dave, A. Dhabale and S. Krishnama Raju, “A Two Stage Approach of Route Planning for UAVs in Logistic Distribution,” 2024 International Conference on Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), Malappuram, India, 2024, pp. 1-6, doi: 10.1109/ICFCR64128.2024.10763026.
2A. Singhal, H. M. Suryawanshi, K. R. Suraparaju, P. P. Nachankar, D. Govind and C. L. Narayana, “An Improved Dual Fixed Frequency SOGI-PLL For Three-phase Grid-Connected Converter Under Unbalanced Condition,” 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET), Arad, Romania, 2022, pp. 687-691, doi: 10.1109/GlobConET53749.2022.9872473.
3R. Bhandare, K. Vaidya and K. R. Suraparaju, “Grid Synchronization using Machine Learning,” 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 2022, pp. 1-6, doi: 10.1109/MysuruCon55714.2022.9972611.
S.No.Paper Citation
1S. Krishnama Raju and G. N. Pillai, “Design and Implementation of Type-2 Fuzzy Logic Controller for DFIG-Based Wind Energy Systems in Distribution Networks,” IEEE Transactions on Sustainable Energy, vol. 7, no. 1, pp. 345–353, 2016. https://doi.org/10.1109/TSTE.2015.2496170 (ISSN/ISBN : 1949-3029
2S. Krishnama Raju and G. N. Pillai, “Design and real time implementation of type-2 fuzzy vector control for DFIG based wind generators,” Renewable Energy, vol. 88, pp. 40–50, Apr. 2016. https://doi.org/10.1016/j.renene.2015.11.006 ( ISSN/ISBN : 0960-1481
3S Krishnama Raju and GN Pillai, “Type-2 Fuzzy Logic based Robust Control Strategy for Power Sharing in Microgrids with Uncertainties in Operating conditions,” International Transaction on Electrical Energy Systems,27(4),pp. 2294, 2017. https://doi.org/10.1002/etep.2294 (ISSN/ISBN : 2050-7038
4Shihabudheen KV, Raju SK, Pillai GN. “Control for grid‐connected DFIG‐based wind energy system using adaptive neuro‐fuzzy technique“, International transactions on electrical energy systems. 2018 May;28(5):e2526, https://doi.org/10.1002/etep.2526
5Shihabudheen KV, Pillai GN, Krishnama Raju S. “Neuro-Fuzzy Control of DFIG Wind Energy System with Distribution Network“, Electric Power Components and Systems. 2018 Aug 9;46(13):1416-31, ISSN/ISBN:1532-5016