Maged Al-Barashi | Electrical Engineering | Best Researcher Award

Assoc. Prof. Dr. Maged Al-Barashi | Electrical Engineering | Best Researcher Award

Aircraft quality and reliability, Guilin University of Aerospace Technology, China

🌟 Dr. Maged Manea Manea Al-Barashi is an accomplished academic, researcher, and engineer specializing in power systems and electrical engineering. Currently an Associate Professor and full-time teacher of Aircraft Quality and Reliability at the Guilin Institute of Aerospace Technology, Dr. Maged has made significant contributions to the fields of high-speed railway and aircraft power systems. With an extensive academic background and global research exposure, he is recognized for his innovative approaches to improving power quality and efficiency.

Publication Profile

Education

🎓 Dr. Maged holds a Ph.D. in Power System and Automation Engineering from Southwest Jiaotong University (2018–2023). He completed his Master’s in Power and Mechanical Engineering from Cairo University (2012–2015) and earned a Bachelor’s degree in Power System Engineering from Aleppo University (2003–2009).

Experience

💼 Dr. Maged has held various roles, including Senior Technical Engineer at NEDCO, Sales Support Engineer at MAM International, and Lecturer at the Institute of Industrial Technology. Since August 2023, he has been a full-time teacher at the Guilin Institute of Aerospace Technology, where he was promoted to Associate Professor in March 2024. He also serves as a research assistant and is actively involved in reviewing for prestigious journals like IEEE JESTPE, ISA Transactions, and IET Power Electronics.

Awards and Honors

🏆 Dr. Maged has received numerous certificates of excellence throughout his academic journey, alongside recognition for his international contributions, such as being a member of the Executive Committee of the Yemeni Students Association in China (2021–2022). He has been honored by the Yemeni Embassy for his exceptional academic achievements and service.

Research Focus

🔬 Dr. Maged’s research is focused on improving power quality in high-speed railway systems, aircraft power systems, and grid-connected converters. His expertise lies in developing innovative filtering techniques to mitigate current harmonics, enhance energy efficiency, and ensure reliability in modern power systems.

Conclusion

🌍 Dr. Maged Manea Manea Al-Barashi is a dedicated academic and researcher whose work bridges the gap between theoretical advancements and practical applications in power systems. His passion for improving power quality and reliability continues to drive impactful contributions to the fields of electrical and aerospace engineering.

Publications

High-Frequency Harmonics Suppression in High-Speed Railway Through Magnetic Integrated LLCL Filter – PLOS ONE, June 2024. DOI: 10.1371/journal.pone.0304464. Cited by 12.

Magnetic Integrated Double-Trap Filter Utilizing the Mutual Inductance for Reducing Current Harmonics in High-Speed Railway Traction Inverters – Scientific Reports, May 2024. DOI: 10.1038/S41598-024-60877-Y. Cited by 15.

Enhancing Power Quality of High-Speed Railway Traction Converters by Fully Integrated T-LCL Filter – IET Power Electronics, April 2023. DOI: 10.1049/pel2.12415. Cited by 8.

Magnetic Integrated LLCL Filter with Resonant Frequency Above Nyquist Frequency IET Power Electronics, October 2022. DOI: 10.1049/pel2.12313. Cited by 10.

Optimizing Solar Power Efficiency in Smart Grids Using Hybrid Machine Learning Models for Accurate Energy Generation Prediction – Scientific Reports, July 2024. DOI: 10.1038/s41598-024-68030-5. Cited by 18.

Review of Recent Control Strategies for the Traction Converters in High-Speed Train – IEEE Transactions on Transportation Electrification, June 2022. DOI: 10.1109/TTE.2022.3140470. Cited by 22.

Improving Power Quality in Aircraft Systems: Usage of Integrated LLCL Filter for Harmonic Mitigation – IEEE 3rd International Conference on Energy and Electrical Power Systems (ICEEPS), July 2024. DOI: 10.1109/ICEEPS62542.2024.10693264.

Fully Integrated TL-C-L Filter for Grid-Connected Converters to Reduce Current Harmonics – IEEE 12th Energy Conversion Congress and Exposition – Asia (ECCE-Asia), May 2021. DOI: 10.1109/ECCEAsia49820.2021.9479097.

A Novel Window Function for Memristor Model with Short-Term and Long-Term Memory Behavior – IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT), July 2024. DOI: 10.1109/ICEICT61637.2024.10671129.

Evaluating the Energy System in YemenJournal of Electrical Engineering (JEE), January 2016.

Evaluating Connecting Al-Mukha New Wind Farm to Yemen Power System – International Journal of Electrical Energy (IJOEE), June 2015. DOI: 10.12720/ijoee.3.2.57-67.

Jian Sun | Smart Grid Control | Best Researcher Award

Assoc. Prof. Dr. Jian Sun | Smart Grid Control | Best Researcher Award

Associate Professor, Southwest University, China

Jian Sun is an Associate Professor in the School of Electronic and Information Engineering at Southwest University, Chongqing, China. With a strong academic and research background in automation and electrical engineering, his work focuses on control systems, reinforcement learning, and grid frequency regulation. Over the years, he has made significant contributions to the field through his publications and innovative approaches to tackling complex power grid challenges. 📚🔬

Publication Profile

ORCID

Education

Jian Sun earned his Ph.D. in Automation from Chongqing University in December 2014. He also completed a visiting Ph.D. program at the University of Wisconsin-Madison, USA, in 2014, specializing in Electrical and Computer Engineering. Prior to his doctoral studies, he obtained a Master’s degree in Automation and a Bachelor’s degree in the same field from Chongqing University. 🎓🌍

Experience

Jian Sun has extensive academic and research experience, currently serving as an Associate Professor at Southwest University. His expertise spans areas like frequency regulation in power systems, energy storage systems, and adaptive control techniques. He has published numerous papers in prestigious journals and has contributed to several interdisciplinary research projects. His work often combines advanced reinforcement learning techniques with cyber-physical systems. 💼🔧

Awards and Honors

Throughout his career, Jian Sun has received recognition for his outstanding research and contributions to the field. His work has been widely cited and appreciated by both academic and industry professionals. He continues to push the boundaries of research in smart grids, energy management, and reinforcement learning. 🏆📈

Research Focus

Jian Sun’s research focuses on developing adaptive and resilient control strategies for smart grids, particularly in the context of frequency regulation. His work includes the integration of Vehicle-to-Grid (V2G) technologies, reinforcement learning for DoS attack resilience, and advanced control systems for energy-efficient power grids. He aims to improve the stability and security of power systems in the face of cyber threats and dynamic load conditions. ⚡🧠

Conclusion

Jian Sun’s academic journey and research have contributed to advancements in smart grid technology, power system regulation, and control theory. His continued dedication to addressing critical challenges in energy systems positions him as a leading figure in his field. His research aims to make power systems smarter, more efficient, and resilient to emerging threats. 🌐🔋

Publications 

Load Forecasting for Commercial Buildings Using BiLSTM–Transformer Network and Cyber–Physical Cognitive Control Systems
Published Year: 2024
Journal: Symmetry
Cited by: Crossref

An Adaptive V2G Capacity-Based Frequency Regulation Scheme With Integral Reinforcement Learning Against DoS Attacks
Published Year: 2024
Journal: IEEE Transactions on Smart Grid
Cited by: Crossref

Cooperative Grid Frequency Control Under Asymmetric V2G Capacity via Switched Integral Reinforcement Learning
Published Year: 2024
Journal: International Journal of Electrical Power & Energy Systems
Cited by: Crossref

Resilient Frequency Regulation for DoS Attack Intensity Adaptation via Predictive Reinforcement V2G Control Learning
Published Year: 2024
Journal: IEEE Transactions on Smart Grid
Cited by: Crossref

Safe Online Integral Reinforcement Learning for Control Systems via Controller Decomposition
Published Year: 2023
Journal: Arabian Journal for Science and Engineering
Cited by: Crossref

A DoS Attack-Resilient Grid Frequency Regulation Scheme via Adaptive V2G Capacity-Based Integral Sliding Mode Control
Published Year: 2023
Journal: IEEE Transactions on Smart Grid
Cited by: Crossref

A DoS Attack Intensity-Aware Adaptive Critic Design of Frequency Regulation for EV-Integrated Power Grids
Published Year: 2023
Journal: International Journal of Electrical Power & Energy Systems
Cited by: Crossref

Structural Scheduling of Transient Control Under Energy Storage Systems by Sparse-Promoting Reinforcement Learning
Published Year: 2022
Journal: IEEE Transactions on Industrial Informatics
Cited by: Crossref

A Sparse Neural Network-Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid
Published Year: 2019
Journal: Applied Sciences
Cited by: Crossref

A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning
Published Year: 2018
Journal: Complexity
Cited by: Crossref

Frequency Regulation of Power Systems with Self-Triggered Control under the Consideration of Communication Costs
Published Year: 2017
Journal: Applied Sciences
Cited by: Crossref