Seyed Fariborz Zarei | Application of Power Electronics in Power Grid | Best Researcher Award

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Prof. Seyed Fariborz Zarei | Application of Power Electronics in Power Grid | Best Researcher Award

Dr. Seyed Fariborz Zarei is an Assistant Professor in the Electrical and Computer Engineering Department at Qom University of Technology, Iran. He earned his B.Sc. from Amirkabir University of Technology in 2012, ranking second in his class, followed by an M.Sc. and Ph.D. from Sharif University of Technology in 2014 and 2019, respectively. In 2018, he was a Visiting Ph.D. Scholar at Aalborg University, Denmark. Since 2021, he has been a faculty member at Qom University of Technology, where he established the Power Electronics and Grid Laboratory. He has authored over 40 research papers focusing on power electronics and electrical power engineering. His research emphasizes modeling, control, protection, and stability of power grids. Dr. Zarei has received numerous awards, including the Kezemi Ashtiani Award and the “Selected Researcher” award.

Profile

Education 🎓

Dr. Zarei holds a Ph.D. in Electrical Engineering (2019) from Sharif University of Technology, focusing on inverter-based AC microgrids’ protection. He was an International Visiting Graduate Student at Aalborg University, Denmark (2018), researching power electronic-based power systems. He completed his M.Sc. in Electrical Engineering (2014) at Sharif University of Technology, specializing in microgrid protection. His B.Sc. in Electrical Engineering (2012) was from Amirkabir University of Technology, where he ranked second in the Power Systems track. His academic excellence earned him a merit-based M.Sc. admission and a Research and Technology Scholarship from the Iran National Science Foundation.

Experience 👨‍🏫

Dr. Zarei is the Director of the Power Electronics and Grid Lab at Qom University of Technology (2022-present), leading research on DC/AC converters and power grid interactions. He has designed and implemented a 2 kVA Grid-Simulator. Previously, he was a Research Assistant at Iran’s National Elites Foundation (2015-2018) and conducted experiments on inverter control at Aalborg University (2018). As a lecturer, he has taught courses on power electronics, electrical machines, reactive power control, and high-voltage substation design.

Research Interests 🔬

Dr. Zarei’s research focuses on power electronics applications in power grids, emphasizing modeling, control, protection, and stability. He investigates DC/AC converters, inverter-based AC microgrid protection, and distributed secondary control schemes. His work includes developing fault detection methods, overvoltage mitigation strategies, and high-impedance fault detection for DC microgrids. He has contributed to improving grid stability through advanced converter control techniques. His research extends to energy optimization in metro systems and power routing-based protection in HVDC grids. His findings have been published in top journals, including IEEE Transactions and Electric Power Systems Research. 🚀

Dr. Zarei received the prestigious Kezemi Ashtiani Award from Iran’s National Elite Foundation (2023) for outstanding academic and research contributions. He was recognized as the “Selected Researcher” in Electrical and Computer Engineering at Qom University of Technology (2022). He earned a Research and Technology Scholarship (2017) and was elected as a member of Iran’s National Elites Foundation (2015-2018). His academic achievements include ranking 17th in the Ph.D. KONKOOR (2014), merit-based M.Sc. admissions (2012), ranking 2nd in his B.Sc. program (2012), and securing 373rd place among 129,000 candidates in the B.Sc. KONKOOR (2008).

Publications 📚

Selvakumaran | EEE | Best Researcher Award

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Dr. Selvakumaran | EEE | Best Researcher Award

Dr. S. Selvakumaran is an accomplished academician with 15 years of experience in engineering education, specializing in power electronics, smart grids, and renewable energy systems. He has held key institutional roles, including NBA, NAAC, Exam Cell, and ISO Coordinator. With a strong research focus on green energy applications, he has published in reputed journals and conferences. His expertise spans control systems, electrical machines, and metaheuristic optimization techniques for power converters. He has guided over 80 UG and 15 PG students, contributing significantly to academia.

Profile

Education 🎓

Dr. Selvakumaran earned his Ph.D. in Electrical Engineering from Anna University in 2024, focusing on optimization-based converters for green energy. He completed his M.E. in Power Electronics and Drives from Government College of Engineering, Tirunelveli (2009) with 73% and his B.E. in Electrical and Electronics Engineering from Dhanalakshmi Srinivasan Engineering College, Perambalur (2007) with 72%. His academic journey reflects his deep commitment to power electronics and renewable energy research.

Experience 👨‍🏫

Dr. Selvakumaran served as an Assistant Professor at Dhanalakshmi Srinivasan Engineering College, Perambalur (2009-2020), mentoring students in electrical engineering. From 2021 to 2024, he was a full-time research scholar at Alagappa Chettiar Government College of Engineering and Technology, Karaikudi, focusing on optimization algorithms for energy applications. His expertise includes NBA and NAAC accreditation, exam cell coordination, and institutional quality management.

Research Interests 🔬

Dr. Selvakumaran’s research focuses on metaheuristic optimization algorithms for power converters, renewable energy integration, and smart grids. He has worked extensively on hybrid energy systems, solar PV optimization, and intelligent power management. His studies include grid-connected electric vehicle systems, power quality improvement, and AI-driven energy optimization, contributing to sustainable energy solutions.

Dr. Selvakumaran has been recognized for his contributions to electrical engineering through best paper awards at national and international conferences. His publications in high-impact journals like the Journal of Energy Storage (IF: 8.9) and IETE Journal of Research (IF: 1.59) highlight his research excellence. He has received appreciation for mentoring students and serving in key academic roles, enhancing institutional accreditation standards.

Publications 📚

  • Optimal planning of photovoltaic, wind turbine and battery to mitigate flicker and power loss in distribution network

    Journal of Energy Storage
    2025-04 | Journal article
    DOI: 10.1016/j.est.2025.116034
    Part ofISSN: 2352-152X
    CONTRIBUTORS: G. Muralikrishnan; K. Preetha; S. Selvakumaran; P. Hariramakrishnan

  • A hybrid approach for PV based grid tied intelligent controlled water pump system

    International Journal of Adaptive Control and Signal Processing
    2024 | Journal article
    DOI: 10.1002/acs.3748
    EID:

    2-s2.0-85184388143

    Part ofISSN: 10991115 08906327
    CONTRIBUTORS: Selvakumaran, S.; Baskaran, K.

  • A Hybrid RBFNN-SPOA Technique for Multi-Source EV Power System with Single-Switch DC-DC Converter

    IETE Journal of Research
    2024 | Journal article
    DOI: 10.1080/03772063.2024.2352644
    EID:

    2-s2.0-85198697794

    Part ofISSN: 0974780X 03772063
    CONTRIBUTORS: Selvakumaran, S.; Baskaran, K.

  • Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

    Journal of Renewable and Sustainable Energy
    2024 | Journal article
    DOI: 10.1063/5.0220051
    EID:

    2-s2.0-85208946652

    Part of ISSN: 19417012
    CONTRIBUTORS: Muralikrishnan, G.; Preetha, K.; Selvakumaran, S.; Nagendran, J.

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xi’an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ：25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
    Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
    10.1109/access.2024.3368755.
  • 4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
    24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
  • [5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929