B.Tech (Electrical Engineering) – VSSUT Burla

M.Tech (Power Systems Engineering) – NIT Warangal

Ph.D (Electrical Engineering) – IIT Bhubaneswar

AI Techniques in Modern Electrical Energy Systems

Teaching – Since Oct 2014

Industry – 3 Years

B.Tech (Power System Protection, Smart Grid, Network Theory, Control Systems)

M. Tech (Smart Electrical Energy System)

Six Sigma Green belt certified professional

Reviewers of reputed journals

ML/DL approaches towards an energy aware building and a smarter  electric grid

M.Tech: Graduated (03)

Current Positions:

1. VP – Athletics and Sports Council
2. Faculty Counselor-IEEE Student Branch
3. NBA/NAAC/NIRF/AICTE Coordinator (EE)

Past Positions:

1. Assistant Superintendent – Exams (2015-2017)
2. Co-VP – Sports Activities (2016-2020)
3. Nodal Officer, Finance – TEQIP-III (2018-2020)
4. Care Taker Officer – NCC (2017-2018)
5. Faculty coordinator – Department society VOLTECH

1. Member – IEEE (Institute of Electrical and Electronics Engg.)     Membership ID- 97509122
2. Life Member – ISTE (Indian Society of Technical Education)     Membership ID- 138092
3. Member – IAENG (International Association of Engineers)
4. Member – ISA (International Society of Automation)

Journal & Conference Papers

1. S. P. Nanda, P. Kalyan K, S. Dash, and P. Mallick, “State-aware household appliance health monitoring system using graph convolutional network conditioned transformer autoencoder,” Engineering Research Express, vol. 7, no. 4, Art. no. 0453a8, 2025.
2. M. R. Sial, O. Sharma, and S. Dash, “Integrated modelling, simulation, and experimental analysis of multi-algorithm torque ripple reduction in switched reluctance motor drives,” Proceedings of the Indian National Science Academy, pp. 1–28, 2025.
3. S. Dash and N. C. Sahoo, “Simulated smart home: A virtual laboratory for synthetic data generation for non-intrusive appliance load monitoring studies,” Electrical Engineering, vol. 107, no. 8, pp. 10587–10606, 2025.
4. S. Dash and N. C. Sahoo, “Non-intrusive appliance energy and health monitoring using attention-assisted deep learning model and dynamic time warping distance-based algorithm,” IEEE Transactions on Instrumentation and Measurement, 2025.
5. S. Dash and N. C. Sahoo, “A multi-task deep learning approach for non-intrusive load monitoring of multiple appliances,” IEEE Transactions on Smart Grid, vol. 15, no. 3, pp. 3337–3340, 2024.
6. S. Dash and N. C. Sahoo, “Attention-based deep learning approach for nonintrusive and simultaneous state detection of multiple appliances in smart buildings,” IEEE Journal of Emerging and Selected Topics in Industrial Electronics, vol. 5, no. 3, 2023.
7. O. Sharma, S. Dash, and M. R. Sial, “A CNN and multi-head attention-based deep learning network for trajectory prediction of autonomous vehicles on multi-lane highways,” in Proc. 4th IEEE Global Conf. for Advancement in Technology (GCAT), 2023, pp. 1–6.
8. S. Dash and N. C. Sahoo, “Attention-based multitask probabilistic network for nonintrusive appliance load monitoring,” IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1–12, 2023.
9. S. Dash and N. C. Sahoo, “Multi-head attention based model for non-intrusive appliance state detection in smart buildings,” in Proc. IEEE 1st Industrial Electronics Society Annual Online Conf., 2022.
10. S. Dash and N. C. Sahoo, “Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review,” Electric Power Systems Research, vol. 213, Art. no. 108673, 2022.
11. S. Dash and N. C. Sahoo, “Deep sequence-to-point learning for electric appliance energy disaggregation in smart building,” in Proc. IEEE Region 10 Symp. (TENSYMP), 2022, pp. 1–6.
12. Y. J. Reddy, Y. V. P. Kumar, A. Ramsesh, S. Dash, and K. P. Raju, “Monitoring and power scheduling of a microgrid with distributed real-time controllers in dynamically simulated environment,” in Proc. IEEE Power India Conf., 2012, pp. 1–6.
13. Y. J. Reddy, S. Dash, A. Ramsesh, Y. V. P. Kumar, and K. P. Raju, “Monitoring and control of real-time simulated microgrid with renewable energy sources,” in Proc. IEEE Power India Conf., 2012, pp. 1–6.
14. I. Kulkarni and S. Dash, “Development of neural network based HVDC controller for transient stability enhancement of AC/DC system,” in Proc. IEEE Students’ Conf. Electrical, Electronics and Computer Science, 2012, pp. 1–6.
15. Y. J. Reddy, S. Dash, Y. V. P. Kumar, and K. P. Raju, “Performance analysis and comparison of power quality indices for hybrid power systems with alternate modes of energy source integration,” in Proc. Int. Conf. Control System and Power Electronics (CSPE), 2012.
16. Y. J. Reddy, S. Dash, Y. V. P. Kumar, and K. P. Raju, “Modeling, optimal sizing and energy management of hybrid power systems for buildings,” in Proc. Int. Conf. Control System and Power Electronics (CSPE), vol. 3, pp. 558–563, 2012.
17. Y. J. Reddy, S. Dash, Y. V. P. Kumar, and K. P. Raju, “Comparison of power quality indices for hybrid power systems with inverter vs. MG set mode of grid integration,” in Proc. Int. Conf. Control System and Power Electronics (CSPE), vol. 3, pp. 572–577, 2012.

Patents

1. B. P. Ganthia, S. Choudhury, S. Dash, S. Mohanty, J. K. Sahu, et al., “Dynamic interaction of DFIG and FSIG induction wind turbines connected to the same distribution feeder,” Indian Patent 03/2022, 2022.
2. B. P. Ganthia, S. Choudhury, S. Mohanty, S. Dash, et al., “Active control applied to wind power for attenuating torque,” Indian Patent 50/2021, 2021.

1. 1st International Conference on “Artificial Intelligence and Machine Learning in Communication and Power System” (AIMLCPS-2026), IEEE Record # 68702.

Room No # 220

Department of Electrical Engineering

PMEC, Sitalapalli, Berhampur,  Odisha, 761003