Dr. Fei Long | Power Systems | Most Cited Article Award

Published on by Computer Scientists Awards

Dr. Fei Long | Power Systems | Most Cited Article Award

Lecturer, China Three Gorges University, China

Dr. Fei Long is a dedicated Lecturer and Postdoctoral Researcher at the School of Electrical Engineering and New Energy, China Three Gorges University 🇨🇳. With a deep-rooted passion for systems control and stability, he has significantly contributed to the advancement of control theory, especially in time-delay and power systems. Known for his academic rigor and innovation, Dr. Long is actively engaged in top-tier research that addresses critical challenges in modern power systems. 📊🔋

Publication profile

Scopus

🎓 Education Background

Dr. Long earned his Ph.D. in Control Science and Engineering from China University of Geosciences (Wuhan) 🎓 between September 2016 and June 2022. Prior to this, he completed his undergraduate studies in Electrical Engineering and Automation at Hubei University of Technology from 2011 to 2015 ⚡📚.

💼 Professional Experience

Since June 2022, Dr. Fei Long has been serving as a Lecturer and Postdoctoral Researcher at China Three Gorges University. His role involves both teaching and conducting cutting-edge research in the field of electrical engineering and control systems. 🏫🔍

🏆 Awards and Honors

Dr. Long is the Principal Investigator of a National Natural Science Foundation of China (Youth Science Fund Project) 🧪. Notably, two of his research papers have been recognized as ESI Highly Cited Papers, placing them in the top 1% of citations worldwide 🌍📈—a testament to the global impact of his research.

🔬 Research Focus

Dr. Long’s primary research interests lie in the stability and robust control of time-delay systems and power systems ⏳⚙️. His work emphasizes innovative mathematical modeling and control strategies to enhance system resilience and performance, particularly in neural networks and energy systems integration.

✅ Conclusion

With a robust academic background, impactful research contributions, and a strong focus on systems stability, Dr. Fei Long stands out as a prominent young researcher in the field of electrical and control engineering. His commitment to excellence and scholarly achievement continues to inspire and shape the future of sustainable energy systems. 🚀📘

📚 Top Publication Notes

IEEE Transactions on Cybernetics (2022) Highly cited paper, recognized for its innovation in delay systems control (Cited by 100+ articles).

IEEE Transactions on Neural Networks and Learning Systems (2023) – Advanced delay-product-type functionals (Cited by 80+ articles).

Automatica (2020) – A key contribution to relaxed matrix inequalities (Cited by 120+ articles).

IEEE Transactions on Systems, Man, and Cybernetics: Systems (2021) – Used in applied power systems and neural networks (Cited by 90+ articles).

Applied Mathematics and Computation (2019) –  Mathematical advancements in delay control theory (Cited by 85+ articles).

IEEE Transactions on Cybernetics (2024) –  Recent breakthrough in delayed neural networks stability.

Dr. Dawei Qiu | Smart Grid | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Dawei Qiu | Smart Grid | Best Researcher Award

Lecturer, University of Exeter, United Kingdom

Dr. Dawei Qiu is a distinguished scholar in smart energy systems, currently serving as a Lecturer at the University of Exeter, UK 🏫. With a strong background in electrical engineering and power systems, he specializes in AI-driven reinforcement learning, market design for low-carbon energy transition, and resilience enhancement of energy systems ⚡. His extensive research contributions in smart grids and power systems have earned him recognition in academia, with a Google Scholar citation count of 2,109, an h-index of 24, and an h10-index of 35 📊.

Publication Profile

Google Scholar

🎓 Education

Dr. Qiu holds a Ph.D. in Electrical Engineering from Imperial College London (2016–2020) 🎓, where he conducted pioneering research on local flexibility’s impact on electricity retailers under the supervision of Prof. Goran Strbac. Prior to this, he completed his M.Sc. in Power System Engineering from University College London (2014–2015) and obtained his B.Eng. in Electrical and Electronic Engineering from Northumbria University at Newcastle (2010–2014) ⚙️. His academic journey has been shaped by esteemed mentors, including Dr. Ben Hanson and Dr. Zhiwei (David) Gao, IEEE Fellow.

💼 Experience

Dr. Qiu’s professional career spans academia and research institutions, where he has contributed significantly to energy systems innovation 🌍. Before joining the University of Exeter in 2024, he was a Research Fellow at Imperial College London (2023–2024), specializing in market design for low-carbon energy systems. He also served as a Research Associate at the same institution from 2020 to 2023 🔬. His work in smart grids and energy resilience has been instrumental in shaping sustainable and intelligent power infrastructure.

🏆 Awards and Honors

Dr. Qiu’s research excellence has been acknowledged through various accolades 🏅. His contributions to smart energy systems, AI-driven reinforcement learning, and low-carbon market design have positioned him as a leading researcher in the field. His studies have been published in top-tier journals, and his work has received high citations, demonstrating its impact on the global research community 🌟.

🔬 Research Focus

Dr. Qiu’s research is centered on leveraging artificial intelligence and reinforcement learning for power and energy applications 🤖. His work explores market mechanisms for cost-effective and sustainable energy transitions, as well as the resilience enhancement of energy systems in response to climate change 🌍. His expertise in AI-driven optimization and machine learning applications in energy systems makes him a key contributor to the advancement of smart grid technologies.

🔚 Conclusion

Dr. Dawei Qiu is a leading researcher in smart energy systems, with a strong academic background and impactful contributions to power systems engineering 🔬. His expertise in AI-driven market optimization, reinforcement learning, and resilient energy systems has made him a valuable asset to the research community 🌍. With his ongoing work at the University of Exeter, he continues to drive innovation in low-carbon and intelligent energy solutions ⚡.

🔗 Publications

A knowledge-based safe reinforcement learning approach for real-time automatic control in a smart energy hub – Applied Energy (Under review, 2025) 🔗 Link

Enhanced Meta Reinforcement Learning for Resilient Transient Stabilization – IEEE Transactions on Power Systems (Under review, 2025) 🔗 Link

Machine learning-based economic model predictive control for energy hubs with variable energy efficiencies – Energy (First round revision, 2024) 🔗 Link

A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems – Proceedings of the IEEE (Under review, 2025) 🔗 Link

Coordinated Optimal Dispatch Based on Dynamic Feasible Operation Region Aggregation – IEEE Transactions on Smart Grid (First round revision, 2024) 🔗 Link

A Sequential Multi-Agent Reinforcement Learning Method for Coordinated Reconfiguration of Substation and MV Distribution Networks – IEEE Transactions on Power Systems (Under review, 2024) 🔗 Link

Enhancing Microgrid Resilience through a Two-Layer Control Framework for Electric Vehicle Integration and Communication Load Management – IEEE Internet of Things Journal (Under review, 2024) 🔗 Link

Coordinated Electric Vehicle Control in Microgrids Towards Multi-Service Provisions: A Transformer Learning-based Risk Management Strategy – Energy (Under review, 2024) 🔗 Link

Adaptive Resilient Control Against False Data Injection Attacks for a Multi-Energy Microgrid Using Deep Reinforcement Learning – IEEE Transactions on Network Science and Engineering (Under review, 2024) 🔗 Link