Dr. Yuxiang Leng | Electrical engineering | Best Researcher Award

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Dr. Yuxiang Leng | Electrical engineering | Best Researcher Award

Ph.D, Chongqing University, China

Yuxiang Leng is an emerging researcher in the field of 3D laser point cloud technology and power transmission systems. He is currently pursuing his Ph.D. at the State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, China. With a strong academic background and an innovative mindset, Leng has actively contributed to the advancement of computer vision applications in power systems. He has published eight academic papers and presented his work at high-level international conferences, gaining recognition in both academia and industry for his contributions to smart grid technologies and engineering digitalization.

Publication Profile

Scopus

🎓 Education Background:

Yuxiang Leng earned his B.Sc. degree from Jiangsu University of Science and Technology in 2019 and his M.S. degree from Wenzhou University in 2022. He is currently a Ph.D. candidate at Chongqing University, focusing on advanced research in the digital management of power substations using 3D vision and laser scanning methods. His educational path reflects a consistent pursuit of excellence in engineering and technology.

💼 Professional Experience:

Though still in the early stages of his research career, Yuxiang has shown remarkable productivity. He has been actively involved in eight research projects and has taken a lead role in multiple academic studies. His technical work centers around developing high-precision measurement techniques and improving data accuracy in laser-based 3D reconstruction. While he has not yet been involved in consultancy or editorial positions, his practical approach and novel methodologies have already earned him significant attention in academic forums.

🏅 Awards and Honors:

While specific awards are not listed, Yuxiang’s recognition as a leading contributor to peer-reviewed SCI/EI journals, including being the first author in three high-impact publications, underlines his academic excellence. Additionally, his oral presentations at top-tier international conferences have positioned him as a rising talent in power equipment digitalization. His selection as an IEEE Student Member further reflects his commitment to research and innovation.

🔬 Research Focus:

Yuxiang’s research lies at the intersection of 3D computer vision, laser point cloud data analysis, and power transmission and transformation systems. His most notable contribution involves a dynamic compensation method that significantly reduces vibration-induced errors in 3D point cloud data, enhancing segmentation and reconstruction precision by over 70%. His work not only advances digital twin modeling but also supports smart maintenance, monitoring, and intelligent management of electrical substations.

🔚 Conclusion:

Yuxiang Leng stands out as a dynamic and promising young researcher whose innovative solutions are driving the future of power system digitalization. With a strong publication record, hands-on project experience, and a clear research vision, he is a fitting candidate for the Best Researcher Award. His blend of academic rigor and practical innovation ensures a meaningful impact in the realm of intelligent power grid technology.

📚 Top Publications of Yuxiang Leng

  1. Dynamic compensation approach for mitigating vibration interference in 3D point cloud data of electrical equipment
    Published: 2025
    Journal: Advanced Engineering Informatics
    Cited by: 3 articles

  2. Intelligent Early Warning System for Power Operation Safety Based on Laser Point Cloud Sensing
    Published: 2024
    Conference Paper: International Conference on Smart Energy Systems
    Cited by: 2 articles

  3. LoRaWAN Network Downlink Routing Control Strategy Based on the SDN Framework and Improved ARIMA Model
    Published: 2023
    Journal: Journal of Communications and Networks
    Cited by: 1 article

  4. Contactless Voltage Measurement Considering Spatially Dependent Voltage Compensation
    Published: 2023
    Conference Proceedings: IEEE Power & Energy Society Meeting
    Cited by: 0 articles

 

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

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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. Caixin Yan | Power System Optimization | Best Researcher Award

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Dr. Caixin Yan | Power System Optimization | Best Researcher Award

PhD Student, Central South University, China

Dr. Caixin Yan is a distinguished researcher at the National Engineering Research Centre of Advanced Energy Storage Materials in Changsha, China. With a deep passion for energy systems and artificial intelligence applications in power grids, Dr. Yan has contributed significantly to the field of energy optimization and power market strategies. His expertise in reinforcement learning and grid stability has made him a prominent figure in the domain of advanced energy storage and smart grid technologies.

Publication Profile

ORCID

🎓 Education:

Dr. Yan pursued his higher education in automation and electrical engineering, focusing on intelligent power grid management and optimization. His academic journey has equipped him with extensive knowledge in multi-energy systems, deep reinforcement learning, and industrial load flexibility.

💼 Experience:

Currently associated with the National Engineering Research Centre of Advanced Energy Storage Materials, Dr. Yan has also collaborated with institutions like the School of Automation at Central South University and the Hunan Xiangjiang Artificial Intelligence Academy. His research focuses on optimizing power systems through artificial intelligence and developing cutting-edge solutions for market-based power regulation.

🏆 Awards and Honors:

While specific awards and honors are not listed, Dr. Yan’s impactful contributions to energy storage, power market strategies, and reinforcement learning applications have been recognized through his publications and collaborations. His research is gaining traction, as evidenced by his growing citation count.

🔍 Research Focus:

Dr. Yan’s research revolves around power grid optimization, energy storage integration, and AI-driven solutions for smart grids. His work on hierarchical reinforcement learning for power grid topology regulation and multi-energy systems operation strategies has been instrumental in advancing the field of intelligent energy management.

🔚 Conclusion:

Dr. Caixin Yan is a rising expert in energy storage and AI-driven power grid optimization. His contributions to power market strategies, reinforcement learning applications, and energy system integration are paving the way for a smarter and more efficient electricity landscape. With growing recognition and impactful research, he continues to make significant strides in the field of intelligent energy solutions. 🚀

📚 Publications :

Review of Power Market Optimization Strategies Based on Industrial Load Flexibility – Analyzing the role of industrial flexibility in power markets.

Power Grid Topology Regulation Method Based on Hierarchical Reinforcement Learning – Exploring AI-driven strategies for grid topology adjustments.

Deep Reinforcement Learning for Strategic Bidding in Incomplete Information Market – Applying AI to strategic bidding in uncertain energy markets.

Optimal Operation Strategies of Multi-Energy Systems Integrated with Liquid Air Energy Storage Using Information Gap Decision Theory – Investigating operational strategies for multi-energy systems.

Load Frequency Control of Photovoltaic Generation-Integrated Multi-Area Interconnected Power Systems Based on Double Equivalent-Input-Disturbance Controllers – Developing control mechanisms for PV-integrated power systems.