Jin Wang | Renewable Energy Technologies Award | Best Researcher Award

Dr. Jin Wang | Renewable Energy Technologies Award | Best Researcher Award

Doctoral candidate, Taiyuan University of Technology, China

Jin Wang, a doctoral candidate at Taiyuan University of Technology, is a promising researcher in the field of electrical engineering. Born on June 1, 1996, Jin is a member of the Han nationality and is deeply focused on coastal renewable energy generation. He is working towards his Ph.D. in Electrical Engineering and has actively contributed to advancing knowledge in energy systems. With a strong academic foundation and hands-on research experience, Jin is making significant strides in his field. 🧑‍🎓⚡🌍

Publication Profile

ORCID

Education:

Jin Wang completed his undergraduate degree in Electrical Engineering at Taiyuan University of Technology in 2020. Following this, he embarked on his Ph.D. journey at the same university, with an expected completion date in 2025. His academic career is distinguished by a dedication to innovation and the pursuit of sustainable energy solutions. 🎓🔌

Experience:

Jin Wang has been involved in a variety of research projects at Taiyuan University of Technology, with a focus on energy systems for polar regions and coastal renewable energy generation. He has also gained practical experience by contributing to national and provincial-level projects, particularly on clean, low-carbon energy systems. 💼🌱

Awards and Honors:

Jin has received several certifications that highlight his commitment to excellence. These include his achievements in English proficiency (CET-6, CET-4), as well as certifications in hydrogen energy technology and industrial technology enhancement. 🏅🎖️

Research Focus:

Jin’s research primarily revolves around coastal renewable energy generation, with specific attention to energy systems in extreme environments, such as polar regions. His work includes projects on energy systems for coastal research stations, hybrid energy systems, and proton exchange membrane fuel cells (PEMFC) in standalone systems. 🔋🌊

Conclusion:

Jin Wang is a dedicated researcher with a clear focus on sustainable energy solutions for challenging environments. His academic background, along with his involvement in cutting-edge research projects, positions him to make significant contributions to the field of renewable energy. 🌍💡

Publications:

Application and effect analysis of renewable energy in a small standalone automatic observation system deployed in the polar regions – AIP Advances, 2022, Author ranking: 1

Improving Proton Exchange Membrane Fuel Cell Operational Reliability Through Cabin-Based Fuzzy Control in Coastal Standalone Observation Systems in Antarctica – Journal of Marine Science and Engineering, 2025, Author ranking: 1

A Multi-Objective Scheduling Strategy for a Hybrid Energy System for Antarctic Coastal Research Stations – Journal of Marine Science and Engineering, 2024, Author ranking: 3

Research on output voltage control of PEMFC based on fuzzy active disturbance rejection – Modern Electronic Technology, 2024, Author ranking: 3

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