Adisu Makeyaw | Power System | Best Researcher Award

Mr. Adisu Makeyaw | Power System | Best Researcher Award

Mr. Adisu Makeyaw , Beijing Jiaotong University , China.

Adisu Makeyaw is a promising young researcher and graduate student at Beijing Jiaotong University, focusing on advanced power systems and railway electrification. With a solid background in electrical engineering and a passion for solving real-world infrastructure problems, Adisu has emerged as a dedicated contributor in the field of smart energy systems. His research has emphasized energy storage, regenerative braking, and stray current mitigation in electrified railways. Through his collaborations with industry partners like the State Grid Shanghai Electric Power Research Institute, he bridges the gap between academic theory and practical solutions for urban power system stability and efficiency.

Publication Profile

ORCID

🎓 Education Background

Adisu Makeyaw pursued his undergraduate and graduate education in electrical and power engineering with a specialization in railway electrification systems. Currently enrolled as a graduate student at Beijing Jiaotong University, he has developed technical expertise in modeling and simulation, especially regarding energy storage systems and control strategies. His thesis, titled “Research on Energy Storage System Scheme of Electrified Railway Traction Substation,” demonstrates his capability in addressing complex engineering challenges. Throughout his academic journey, Adisu has combined analytical rigor with a focus on practical implementation, laying a strong foundation for innovative solutions in the field of sustainable energy.

🧑‍💼 Professional Experience

Though still a student, Adisu Makeyaw has gained early professional recognition through impactful research collaborations. He has worked closely with the State Grid Shanghai Electric Power Research Institute, contributing to transformer DC bias mitigation solutions in metro systems. His work integrates simulation, field data, and theoretical analysis to design real-time monitoring and suppression methods. These experiences not only enhanced his engineering acumen but also strengthened his ability to manage industry-academia collaboration. Adisu continues to expand his professional portfolio by engaging in research that influences policy and technical standards in power distribution networks and urban rail infrastructure.

🏆 Awards and Honors

While formal awards are currently pending, Adisu Makeyaw’s academic contributions are already being acknowledged in reputable scientific platforms. His recent publication in the Energies journal showcases his innovative thinking and has gained scholarly attention. As a member (pending) of the IEEE, he is positioning himself among global professionals in electrical engineering. His impactful work in DC bias mitigation and energy storage system design reflects a commitment to research excellence that is well-aligned with recognition such as the Best Researcher Award. With growing citations and collaboration with leading institutes, Adisu is on a clear trajectory for prestigious accolades in the near future.

🔬 Research Focus

Adisu’s research centers around regenerative braking, energy storage systems, stray current mitigation, and transformer DC bias phenomena within urban rail transit systems. He is especially interested in how electrified railway systems affect utility grid performance and transformer health. His projects explore innovative ways to reduce electromagnetic disturbances, enhance transformer longevity, and stabilize grid voltage under metro-induced interference. A key aspect of his work includes proposing DC-blocking devices, integrated simulation models, and optimization strategies that are grounded in both theory and practice. His current work contributes toward resilient, sustainable, and intelligent urban electrification systems.

🧾 Conclusion

Adisu Makeyaw is a rising researcher whose work bridges the technical gaps in urban electrification and smart energy systems. With a keen eye for real-world applicability and a deep understanding of power electronics, Adisu is making meaningful contributions to infrastructure innovation. His academic rigor, combined with practical collaboration, makes him a strong contender for recognition such as the Best Researcher Award. As he continues to publish, collaborate, and innovate, Adisu stands as a promising figure in the transformation of sustainable railway systems and future-proof power networks.

📄 Top Publication Note

Title: Utility Transformer DC Bias Caused by Metro Stray Current—A Review
Authors: Makeyaw, A.; Yang, X.; Sun, X.; Liu, K.; Wu, T.; Chen, L.
Journal: Energies
Published Year: 2025
Cited by Articles: 3 (as of July 2025 in Google Scholar and Scilit)
Indexed In: SCI, Scopus

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