Manas Ranjan Sethi | ECE | Best Researcher Award

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Mr. Manas Ranjan Sethi | ECE | Best Researcher Award

Research Scholar at NIT Silchar, India

Manas Ranjan Sethi is a dedicated academic professional currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar, with a strong focus on machine learning applications in fault diagnosis and energy systems. He holds an M.Tech in Electronics & Telecommunication from BPUT, Odisha and has over 12 years of teaching experience, having worked as an Assistant Professor at Gandhi Institute for Technology (GIFT) and as a Lecturer at Koustuv Institute of Self Domain, Bhubaneswar. His research interests include machine learning, signal processing, and sustainable energy systems, particularly in wind turbine diagnostics and emotion recognition using EEG signals. Manas has contributed to numerous journals, conferences, and book chapters, and he has earned distinctions such as qualifying CBSE-UGC NET and GATE. He is also skilled in technical tools, enjoys singing, and has a passion for reading.

Publication Profile : 

Scopus

 

🎓 Educational Background :

Manas Ranjan Sethi is currently pursuing a Ph.D. in Electronics and Instrumentation Engineering at NIT Silchar (since March 2020). He holds an M.Tech in Electronics & Telecommunication (Specialization in Communication Engineering) from BPUT, Odisha (2012), with a CGPA of 8.20. He completed his B.E. in Electronics & Telecommunication from BPUT, Odisha, in 2006, securing a 62.19%. Additionally, he completed his +2 Science and Matriculation from MP Board, Madhya Pradesh.

💼 Professional Experience :

Manas has a rich academic career spanning over 12 years. He served as an Assistant Professor in the Electronics & Communication Engineering Department at Gandhi Institute for Technology (GIFT), Bhubaneswar, from November 2013 to March 2020. Prior to that, he worked as a Lecturer in the Electronics & Telecommunication Engineering Department at Koustuv Institute of Self Domain, Bhubaneswar, from July 2007 to November 2013. He has a strong foundation in teaching and mentoring students in the field of Electronics and Communication Engineering.

📚 Research Interests : 

Manas’s research interests lie in the domains of Machine Learning, Signal Processing, and Fault Diagnosis. His work focuses on vibration signal-based diagnostics and energy extraction using wind turbines. He is passionate about leveraging machine learning techniques for predictive maintenance and condition monitoring. His recent research includes the application of meta-classifiers for diagnosing wind turbine blade faults and exploring emotion recognition through EEG signals.

🏆Achievements & Certifications :

Manas has earned several academic distinctions, including qualifying the CBSE-UGC NET (Electronic Science) in July 2018, and securing GATE scores of 260 (2016) and 218 (2011) in Electronics and Communication. He has also attended and contributed to various seminars, workshops, and short-term courses in fields such as VLSI Design, Microwave Filters, and Adaptive Signal Processing.

📝 Publication Top Notes :

  • Sethi, M. R., Subba, A. B., Faisal, M., Sahoo, S., & Koteswara Raju, D. (2024). Fault diagnosis of wind turbine blades with continuous wavelet transform based deep learning model using vibration signal. Engineering Applications of Artificial Intelligence, 138, 109372.
  • Sethi, M. R., Sahoo, S., Dhanraj, J. A., & Sugumaran, V. (2023). Vibration Signal-Based Diagnosis of Wind Turbine Blade Conditions for Improving Energy Extraction Using Machine Learning Approach. Smart and Sustainable Manufacturing Systems, 7(1), 14–40.
  • Chatterjee, S., Sethi, M. R., & Asad, M. W. A. (2016). Production phase and ultimate pit limit design under commodity price uncertainty. European Journal of Operational Research, 248(2), 658–667.
  • Sethi, M. R., Parhi, S. S., Sahoo, S., Sugumaran, V., & Mohanty, S. R. (2023). Fault Diagnosis of Wind Turbine Blades Through Vibration Signal Using Filtered Cultivation Data: A Comparative Study. Proceedings of the 2023 IEEE Region 10 Symposium, TENSYMP 2023.
  • Kar, P., Hazarika, J., & Sethi, M. R. (2023). A Comparative Study between Supervised and Unsupervised Techniques for Two Class Emotion Recognition using EEG. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology, I2CT 2023.
  • Banala, H. S., Sahoo, S., Sethi, M. R., & Sharma, A. K. (2023). Fault Diagnosis in Wind Turbine Blades Using Machine Learning Techniques. In R. Doriya, B. Soni, A. Shukla, & X. Z. Gao (Eds.), Machine Learning, Image Processing, Network Security and Data Sciences (Lecture Notes in Electrical Engineering, Vol. 946), 401–411. Springer, Singapore.
  • Sethi, M. R., Sahoo, S., Kanoongo, S., & Hemasudheer, B. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Rule Classifier. Proceedings of the 2022 2nd International Conference on Emerging Frontiers in Electrical and Electronic Technologies (ICEFEET 2022), 1–6. doi: 10.1109/ICEFEET51821.2022.9848401.
  • Sethi, M. R., Hemasudheer, B., Sahoo, S., & Kanoongo, S. (2022). A Comparative Study on Diagnosing Wind Turbine Blade Fault Conditions using Vibration Data through META Classifiers. Proceedings of the 2022 4th International Conference on Energy, Power, and Environment (ICEPE 2022), 1–5. doi: 10.1109/ICEPE55035.2022.9798026.

 

 

 

Zhang Wenping | Electrical Engineering | Best Researcher Award

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Dr. Zhang Wenping | Electrical Engineering | Best Researcher Award

Chief Expert of Ginlong Technologies Co., Ltd, China

Dr. Wenping Zhang is a distinguished researcher in power electronics and renewable energy systems with a diverse academic and industrial background. Currently at Tianjin University and Ginlong Technologies, he has extensive experience working on advanced power conversion systems for electric vehicles and fuel cell technologies. 🚗⚡🔋 His work bridges the gap between cutting-edge research and real-world applications, contributing significantly to energy storage and smart grid technologies. 🌍🔌

Publication Profile : 

Scopus

 

🎓 Educational Background :

Dr. Wenping Zhang holds a Ph.D. in Electrical Engineering from Zhejiang University, where he worked under the guidance of Prof. Dehong Xu (IEEE Fellow). During his Ph.D. (2011-2016), he also spent time as a visiting scholar at Texas A&M University under the mentorship of Prof. Prasad Enjeti (2012-2013). Prior to this, he completed his Master’s degree at Zhejiang University (2008-2011) under the supervision of Prof. Changsheng Hu and Prof. Dehong Xu. He earned his Bachelor’s degree in Energy and Power Engineering from Nanjing University of Science and Technology in 2008.

💼 Professional Experience :

Professionally, Dr. Zhang has a rich career in both academia and industry. Currently, he is a researcher at Tianjin University and Ginlong Technologies (since July 2022). Before this, he served as a Senior Expert at Siemens China from 2019 to 2022, and earlier, he was a Postdoctoral Research Fellow at the University of New Brunswick, Canada (2017-2019), where he worked under Prof. Liuchen Chang, CAE Fellow. His work primarily focuses on developing technologies for electric vehicles (EV) used as distributed energy storage, fault-tolerant power electronic systems, fuel cell uninterruptible power supplies, and renewable energy systems. He has led and contributed to significant projects like the China ‘863’ program, the Emera R&D Project, and Delta R&D Project, working on cutting-edge solutions in energy and power electronics.

📚 Research Interests : 

His research interests include power electronics, electric vehicles as distributed energy storage systems, fault-tolerant techniques, renewable energy systems, fuel cell technologies, and smart grid integration. Dr. Zhang has published numerous papers in high-impact journals and holds several patents related to power electronics and renewable energy technologies.

📝 Publication Top Notes :

  1. W. Zhang, Y. Wang, P. Xu, D. Li, B. Liu, “Evaluation and Improvement of Backup Capacity for Household Electric Vehicle Uninterruptible Power Supply (EV-UPS) Systems,” Energies, vol. 16, no. 4567, 2023. (SCI)
  2. W. Zhang, Y. Wang, P. Xu, D. Li, B. Liu, “A high-reliability PV system by replacing electrolytic capacitors with film capacitors,” Energy Reports, vol. 9, suppl. 10, pp. 299-305, 2023. (SCI)
  3. W. Zhang, Y. Wang, P. Xu, D. Li, B. Liu, “DC-bus voltage balancing control for 3-level DC/DC converters in renewable generation systems,” Energy Reports, vol. 9, suppl. 10, pp. 210-217, 2023. (SCI)
  4. W. Zhang, Y. Wang, P. Xu, D. Li, B. Liu, “A Current Control Method for Grid-Connected Inverters,” Energies, vol. 16, no. 18, p. 6558, 2023. (SCI)
  5. W. Zhang, P. Xu, Y. Wang, D. Li, B. Liu, “A potential induced degradation suppression method for photovoltaic systems,” Energy Reports, vol. 10, pp. 3955-3969, 2023. (SCI)
  6. W. Zhang, P. Xu, Y. Wang, D. Li, B. Liu, “A DC arc detection method for photovoltaic (PV) systems,” Results in Engineering, vol. 21, 2024, p. 101807. (SCI)
  7. W. Zhang, D. Xu, P. Enjeti, H. Li, J.T. Hawke, H.S. Krishnamoorthy, “Survey on Fault-Tolerant Techniques for Power Electronic Converters,” IEEE Transactions on Power Electronics, vol. 29, no. 12, pp. 6319-6331, Dec. 2014. (Citation: 191)
  8. W. Zhang, D. Xu, X. Li, R. Xie, H. Li, D. Dong, C. Sun, M. Chen, “Seamless Transfer Control Strategy for Fuel Cell Uninterruptible Power Supply System,” IEEE Transactions on Power Electronics, vol. 28, no. 2, pp. 717-729, Feb. 2013. (Citation: 76)
  9. W. Zhang, D. Xu, “Fault Analysis and Fault-Tolerant Design for Parallel Redundant Inverter Systems in Case of IGBT Short-Circuit Failures,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 6, no. 4, pp. 2031-2041, Dec. 2018.
  10. W. Zhang, C. Ding, “Mitigation of the low-frequency neutral-point current for three-level T-type inverters in three-phase four-wire systems,” IET Power Electronics, vol. 11, no. 8, pp. 1444-1451, 2018.