Assist. Prof. Dr. Dogan Celik | Power Electronics | Editorial Board Member

Professor | Van Yuzuncu Yıl University | Turkey

Assist. Prof. Dr. Doğan Çelik is a prominent researcher known for his significant contributions to microgrids, distributed generation, power quality improvement, and electric vehicle (EV) systems. His work integrates advanced control strategies with modern smart grid and renewable energy technologies, supporting the transition toward efficient, reliable, and sustainable power networks. With strong academic experience and active involvement in research and supervision, he has contributed to the advancement of electrical power systems, distributed generation coordination, power electronics, and EV charging and grid-integration methods. His research focuses on microgrid optimization, power quality enhancement, active filtering, smart grid digitalization, EV charging strategies including V2G, and advanced control methods such as sliding mode control, PR controllers, Lyapunov-based techniques, and Kalman filtering. His publications in reputable international journals have received notable citations, reflecting the technical relevance and practical value of his work. Through ongoing research collaborations and innovative contributions, he continues to support global efforts aimed at strengthening modern energy infrastructure and accelerating the deployment of intelligent, sustainable electrical systems.

Featured Publications

1. Mahmud, K., Town, G. E., Morsalin, S., & Hossain, M. J. (2022). Investigation and analysis of effective approaches, opportunities, bottlenecks and future potential capabilities for digitalization of energy systems and sustainable … Electric Power Systems Research, 212, 108251.

2. Abujubbeh, A., Mossa, M. A., & Chaari, O. (2019). A comprehensive survey on control strategies of distributed generation power systems under normal and abnormal conditions. Annual Reviews in Control, 47, 102–117.

3. Çelik, D., Niazi, K. A. K., & Ahmad, M. W. (2022). Lyapunov-based harmonic compensation and charging with three-phase shunt active power filter in electrical vehicle applications. International Journal of Electrical Power & Energy Systems, 137, 107564.

4. Darwish, M. K., Abdelaziz, A. Y., & El-Naggar, K. M. (2019). Current control based power management strategy for distributed power generation system. Control Engineering Practice, 85, 90–104.

5. Çelik, D., Khan, M. A., & Hossain, M. J. (2023). Kalman Filter-Based Super-Twisting Sliding Mode Control of Shunt Active Power Filter for Electric Vehicle Charging Station Applications. IEEE Transactions on Power Delivery, 38(2), 1547–1556.

Mr. Syed Musadiq Mehdi |Power Electronics| Best Researcher Award

Hongik University,South Korea

Mr. Syed Musadiq Mehdi is a distinguished researcher and academic affiliated with Hongik University, South Korea. With expertise spanning [mention specific field or focus area, e.g., artificial intelligence, materials science, etc.], he has made significant contributions through his innovative research and publications. At Hongik University, Mr. Mehdi is actively involved in advancing knowledge and fostering academic excellence. His work reflects a strong commitment to addressing contemporary challenges and driving impactful solutions in his field.

Summary:

Mr. Syed Musadiq Mehdi stands out as a promising researcher with strong academic credentials, impactful publications, and notable technical expertise. His innovative work in power electronics and neural networks positions him as a contender for the award.

Professional Profiles:

Orcid

🎓 Education :

Master’s Degree (Electrical and Electronics Engineering): Currently pursuing at Hongik University, Seoul, South Korea (since March 2023).,Bachelor’s Degree (Electrical Engineering): Graduated from Sharif College of Engineering and Technology, Lahore, Pakistan (2018–2022).

🏢 Experience:

Internship at DG-Cement PVT-Ltd, Pakistan (Aug–Sept 2019):,Gained hands-on experience with coal-fired and waste heat recovery power plants, SCADA systems, and transformer troubleshooting. This role provided foundational knowledge in power systems and electrical infrastructure management.

🛠️Skills:

Technical Skills: MATLAB/Simulink, Python, SQL, NI-MultiSim, Microsoft Office Suite.,Research & Writing Skills: Proficient in policy briefs, essays, and technical paper writing.,Languages: Fluent in English (C1 level), with native proficiency in Urdu.

Research Focus :

Modular Multilevel Converters (MMC): Developing advanced neural network observers to enhance capacitor voltage estimation accuracy in MMCs.,IoT Applications: Innovated a password-protected DC circuit breaker system for HVDC lines, integrating IoT for enhanced operational control.,Power Electronics: Published works on neural network-based estimations for motor drives and capacitor voltage in MMCs, demonstrating significant advancements in real-time applications.

🔬Awards:

Research Ethics Certification (2024): Awarded by the Korean Institute of Human Resource Development in Science and Technology for upholding scientific integrity and promoting ethical research practices.

Conclusion:

Based on the provided credentials and contributions, Mr. Syed Musadiq Mehdi is a suitable candidate for the Research for Best Researcher Award. His strengths, particularly in addressing real-world problems through innovative research, align with the award’s goals. However, enhancing his research breadth and industry engagements could elevate his standing in future evaluations.

Publications:

A Novel Capacitance Estimation Method of Modular Multi-level Converters for Motor Drives Using Recurrent Neural Networks with Long Short-Term Memory
This study introduces an LSTM architecture tailored for high-precision capacitance estimation in PMSM drives. The proposed method captures MMCs’ dynamic behavior effectively, validated through MSE metrics and comparative analysis of actual versus estimated capacitance. Robustness under varied operating conditions confirms its practical applicability for real-time power electronics.

Authors: Syed Musadiq Mehdi, DM Lee
Journal: MDPI Energies

Improved Estimation Method for the Capacitor Voltage in Modular Multilevel Converters Using Distributed Neural Network Observer
This paper proposes a distributed neural network observer to enhance capacitor voltage estimation in MMCs. Utilizing a multi-layer neural network and a distributed approach, the system significantly improves accuracy and robustness.

Authors: Mehdi Syed Musadiq, DM Lee
Journal: Journal of the Institute of Korean Electrical and Electronics Engineers