Danish Khan | Engineering | Best Researcher Award

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Dr. Danish Khan | Engineering | Best Researcher Award

Research Assistant | Zhejiang University | China

Danish Khan is a dedicated researcher and engineer specializing in power systems, optimization, and machine learning applications for renewable energy. With a Ph.D. from Zhejiang University and a master’s from Shanghai Jiao Tong University, his work enhances the performance of grid-connected inverters and battery energy storage systems. Currently serving as a Postdoctoral Fellow at Hong Kong Industrial Artificial Intelligence & Robotics Centre (FLAIR), he advances smart energy systems through control design and AI integration.

Professional Profile

Scopus

Education

Danish Khan is an accomplished researcher in Electrical Engineering, specializing in power electronics and grid integration. He earned his Ph.D. from Zhejiang University (2020–2024), where his research focused on enhancing the stability of LCL-filtered grid-connected inverters a critical aspect of renewable energy systems. Prior to this, he completed his Master’s degree at Shanghai Jiao Tong University (2017–2020), conducting in-depth studies on resonance peak mitigation to improve power quality and system reliability. His academic foundation was laid at COMSATS University, Islamabad, where he earned his Bachelor’s degree in Electrical Engineering (2012–2016). Throughout his academic career, Danish has demonstrated a deep commitment to solving complex problems in energy systems through advanced modeling, control strategies, and system optimization. His work contributes significantly to the advancement of efficient and stable smart grid technologies. With strong analytical skills and a robust technical background, he stands out as a promising expert in sustainable and intelligent energy solutions.

Experience

Danish Khan brings robust hands-on research experience in AI-driven energy systems and embedded control. Currently, he is a Postdoctoral Research Fellow at FLAIR, Hong Kong, where he is actively engaged in the development of robotic-energy integration systems, merging advanced artificial intelligence techniques with power electronics. His work aims to create smarter, adaptive energy systems that align with the future of automation and sustainable technologies. Prior to this, he served as a Research Assistant at Shanghai Jiao Tong University, where he was involved in both academic and technical roles, managing lecture events and spearheading the development of novel inverter control strategies. His contributions during this time significantly advanced control techniques for power electronic converters, enhancing their performance in dynamic grid environments. Danish’s multidisciplinary expertise allows him to bridge AI, robotics, and energy systems positioning him as a valuable contributor in both academic and applied research environments focused on smart grid evolution and intelligent energy solutions.

Research Interests

Danish Khan’s research focuses on adaptive control, optimization, and machine learning within the realm of power electronics. He develops advanced control strategies for grid-connected inverters, solar photovoltaic (PV) systems, and battery energy storage solutions, aiming to enhance system efficiency and stability. His work also extends to glare analysis for infrastructure planning, addressing safety and performance concerns in renewable energy deployment. By integrating AI techniques like reinforcement learning, he strives to build intelligent, self-adaptive systems capable of responding to dynamic environmental and operational conditions. His research supports the development of resilient and efficient green technologies for sustainable energy futures.

Awards

Danish has been recognized with several prestigious awards, including the Chinese Government Scholarship for his Ph.D., the National Endowment Scholarship for Talent during his master’s, and the HEC Pakistan Scholarship for his undergraduate studies. He received a Bronze Medal from COMSATS and an Excellent Oral Presentation Award at an international conference in Shanghai (2019).

Top Noted Publications

Title: Enhanced Stability of Grid-Connected Inverter Using Adaptive Filtering Damping
Year: 2025
Cited by: 12

Title: A Reinforcement Learning-Based Control System for Higher Resonance Frequency Conditions
Year: 2024
Cited by: 17

Title: Half-Quadratic Criterion-Based Adaptive Control for Robust LCL-Filtered Inverter
Year: 2024
Cited by: 10

Title: Optimal LCL-Filter Design Using Metaheuristic Algorithms
Year: 2023
Cited by: 29

Title: Loss Reduction in Isolated Series Resonant Converter
Year: 2022
Cited by: 35

Title: Capacitor Current Resonance Suppression Using ASPR Plant Concept
Year: 2022
Cited by: 28

Conclusion

Danish Khan is a promising and technically advanced researcher whose work addresses some of the most pressing challenges in modern electrical power systems and renewable energy integration. With a solid research portfolio, strong publication record, hands-on experience, and international education, he stands out as a suitable candidate for the Best Researcher Award especially in emerging areas such as AI-powered energy systems and adaptive inverter control.

Xiangming Hu | Engineering | Best Researcher Award

Published on by Environmental Scientists

Prof. Dr. Xiangming Hu | Engineering | Best Researcher Award

Professor, Shandong University of Science and Technology, China

Professor Xiangming Hu is a distinguished academic at the College of Safety and Environmental Engineering, Shandong University of Science and Technology in Qingdao, China. He also serves as the Deputy Director of the State Key Laboratory for Mine Disaster Prevention and Control. His work primarily focuses on developing innovative solutions for mine safety and environmental protection.

Profile​

Scopus

Education 🎓

Professor Hu earned his Ph.D. from the University of Copenhagen, Denmark, in 2014. Prior to that, he completed his M.Sc. at the Graduate University of the Chinese Academy of Sciences in 2011 and his B.Sc. at Nankai University in 2008.​

Experience 🏢

From 2015 to 2019, Professor Hu was a Postdoctoral Fellow at the Interdisciplinary Nanoscience Center, Aarhus University, Denmark. He then served as an Assistant Professor in the Department of Chemistry at Aarhus University from 2019 to 2020. In 2020, he joined Shandong University as a Professor.​

Research Interests 🔬

Professor Hu’s research focuses on the capture and catalytic conversion of carbon dioxide, as well as the catalytic degradation of volatile organic compounds. His work aims to develop sustainable solutions for environmental challenges.​

Awards 🏆

Professor Hu has been recognized with several awards, including the “Qilu Young Scholars” program from Shandong University (2020-2025). He has also received funding from the National Natural Science Foundation of China and the Shandong Provincial Natural Science Foundation.​

Publications 📚

Study on the influence of dry ice phase change behavior on the micropore structure and hydration properties of mining grouting materials based on experiments and molecular simulations
Published in: Construction and Building Materials, 2024
Cited by: Link to article​

Synchronous hydrogen and electricity production by dual-cathodes in a bioelectrochemical system
Published in: Journal of Cleaner Production, 2024
Cited by: Link to article​

​Study on flame propagation and inherent instability of hydrogen/ammonia/air mixture
Published in: Fuel, 2024
Cited by: Link to article​

Study on the cooperation mechanism of urea-hydrolysis bacteria and biosurfactant bacteria for dust suppression
Published in: Chemical Engineering Journal, 2024
Cited by: Link to article​

​Deep integration of pyrolysis kinetics and pyrolysis mechanism of polyimide aerogels
Published in: Fuel, 2024
Cited by: Link to article

🏁 Conclusion

Professor Xiangming Hu is an exceptionally strong candidate for the Best Researcher Award. His research portfolio is not only prolific but also directly aligned with pressing environmental challenges. His leadership position, international academic background, and impactful research outputs make him highly deserving of this honor. A few enhancements in outreach and innovation commercialization would further solidify his global research influence.

Assoc Prof. Dr. hacene mellah | Engineering | Best Researcher Award

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Assoc Prof. Dr. hacene mellah | Engineering | Best Researcher Award

Assoc Prof. Dr. hacene mellah, bouira university, Algeria

Dr. Hacene Mellah is a dedicated researcher and esteemed lecturer in Electrical Engineering, specializing in electrical machines. Currently serving as an MCA at Akli Mohand Oulhadj University in Bouira, Algeria, he brings extensive experience in both academia and research. With a career marked by a strong commitment to advancing electrical engineering, Dr. Mellah contributes actively to various scholarly publications and committees. His insights and research have notably enhanced knowledge in electrical systems, control, and diagnostics.

Profile

Orcid

Education 🎓

Dr. Mellah’s academic journey began with a BAC in 2001, followed by an Engineering degree in Electrical Control from Ferhat Abbas University, Setif, in 2006. He obtained a Magister in Electrical Machines and Control in 2009 and a Doctorate in Electrical Machines in 2020, also from Ferhat Abbas University. His doctoral research focused on enhancing the intrinsic parameters of electrical machines, a pursuit that reflects his commitment to innovation in electrical engineering.

Professional Experience 👨‍🏫

Dr. Mellah has been actively teaching since 2010 across various institutions, including the University of Sétif and the University of Chlef. His teaching portfolio spans power electronics, control systems, and electrical systems design for both undergraduate and graduate levels. His expertise also extends to mentoring students in competitions and supervising theses, demonstrating his dedication to nurturing future engineers.

Research Interests 🔬

Dr. Mellah’s research primarily revolves around electric machine diagnostics, control strategies, and multi-physics modeling. His work explores innovative methods for machine learning diagnostics, thermal transfer, and parametric estimation. He applies advanced techniques such as artificial neural networks to improve the accuracy of diagnostic and control systems, particularly for electric machines like IM, DCM, and PMSG.

Awards 🏆

Throughout his career, Dr. Mellah has been recognized for his contributions to electrical engineering research and academia. His dedication to advancing sustainable and renewable energy systems has earned him respect among peers and accolades from professional organizations. His ongoing involvement with academic journals as an editor and reviewer further reflects his esteemed role in the research community.

Publication Top Notes 📚

Fuzzy Logic-Based Smart Control of Wind Energy Conversion System Using Cascaded Doubly Fed Induction Generator

Performance improvement of a DPC-FPID strategy with matrix converter using CDFIG in wind power system

Optimization of the Powers Exchanged between a Cascaded Doubly Fed Induction Generator and the Grid with a Matrix Converter

Point on Wave Energization Strategy and Sequential Phase Shifting for Sympathetic Inrush Current Mitigation in Three-Phase Transformer – Measurement

Comparative study of tolerant controls used for fault detection in dual-feed machines

Analysis and testing of internal combustion engine driven linear alternator

Improvement of Sliding Mode Control Strategy Founded on Cascaded Doubly Fed Induction Generator Powered by a Matrix Converter

A Fast-Intelligent Sensor Based on Cascade-Forward Neural Network Founded by Resilient Backpropagation for Simultaneous Parameters and State Space Estimation of Brushed DC Machines
Comparing performances of three CFNN used for DC machine combined parameter and states estimation.