Dr. Gu Shan | Power Systems | Women Researcher Award

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Dr. Gu Shan | Power Systems | Women Researcher Award

Associate Professor | Zhejiang University of Water Resources and Electric Power | China

Dr. Shan Gu is a researcher in the fields of energy engineering, sustainable power systems, and environmental technology, with a strong focus on biomass utilization, air-pollutant mitigation, and life cycle assessment. Her work integrates engineering experimentation, process optimization, and environmental impact evaluation to advance the development of clean energy technologies. She has contributed significantly to the study of biomass pyrolysis, nanosilica extraction from agricultural waste, and the operational behavior of circulating fluidized bed gasifiers. Her research on biomass CFB gasification systems, including the coupling of gasifiers with industrial steam boilers, has generated important insights into practical challenges such as slagging, ash deposition, and system optimization. These contributions have provided evidence-based guidance for the scaling, operation, and environmental performance improvement of biomass-based energy systems. Dr. Gu has authored more than 30 research publications, including multiple SCI-indexed articles, with several featured in high-impact journals. Her scholarly work demonstrates strong visibility, with measurable academic influence across citation databases. According to Scopus, she has 14 indexed documents, 16 citations by 15 documents, and an h-index of 2. Her Google Scholar profile shows significantly higher engagement, with over 250 citations across her most influential works, including widely referenced studies on nanosilica production and biomass gasification, each exceeding 100 citations. Her publications continue to inform ongoing research in sustainable materials, renewable energy pathways, and the optimization of energy–environment systems, positioning her as an active contributor to advancing cleaner technologies and carbon-reduction strategies.

Publication Profile

Scopus

Featured Publications

Gu, S., Zhou, J., Luo, Z., Wang, Q., & Ni, M. (2013). A detailed study of the effects of pyrolysis temperature and feedstock particle size on the preparation of nanosilica from rice husk. Industrial Crops and Products. Citations: 121.

Gu, S., Zhou, J., Yu, C., & Shi, Z. (2015). A novel two-staged thermal synthesis method of generating nanosilica from rice husk via pre-pyrolysis combined with calcination. Industrial Crops and Products. Citations: 105.

Gu, S., Zhou, J., Luo, Z., & Shi, Z. (2015). Kinetic study on the preparation of silica from rice husk under various pretreatments. Journal of Thermal Analysis and Calorimetry. Citations: 25.

Gu, S., Zhou, J., Lin, B., & Luo, Z. (2015). Life cycle greenhouse gas impacts of biomass gasification-exhausted heat power generation technology in China. Journal of Biobased Materials and Bioenergy..

Li, R., Gu, S., Ye, Y., Li, Z., Zhou, L., & Xu, C. (2025). System optimization and primary electrical design of a 50 MW agrivoltaic power station: A case study in China.

Dr. Jeonghoon Moon | Power Electronics | Best Researcher Award

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Dr. Jeonghoon Moon | Power Electronics | Best Researcher Award

Dr. Jeonghoon Moon | Visiting Professor in the Department of Electronic Engineer | Chosun University | South Korea

Jeonghoon Moon is a distinguished researcher in power electronics and AI-based control, with a focus on EMI-aware predictive control of DC–DC converters, sensor-level CPS security, and battery balancing strategies. His research integrates advanced machine learning techniques, including physics-informed LSTM models, with practical hardware implementations on DSP platforms for real-time disturbance prediction, ripple reduction, and system stability. He has made significant contributions to predictive and robust control, developing lightweight controllers that approximate LSTM outputs for deterministic execution on embedded systems, enabling faster detection latency and improved DC-rail performance. Moon has proposed novel safety envelopes unifying efficiency deviation with time- and frequency-domain ripple metrics to guide safe derating under dynamic operating conditions and potential spoofing scenarios. His work also encompasses EMI-aware PWM shaping and battery module balancing, validated through rigorous MATLAB/Simulink simulations and reproducible hardware experiments. Moon maintains multi-institutional collaborations with academic and industry partners to advance power electronics and AI integration. His research outputs include four SCI/SCIE journal publications, multiple consultancy projects, and one patent, reflecting both academic rigor and industrial relevance. His research impact is evidenced by 25 Scopus-indexed documents with 25 citations and an h-index of 2. Moon’s contributions extend to ultrasonic piezo resonance tracking and high-speed resonant frequency detection using AI-guided methodologies, demonstrating the applicability of machine learning in real-time control systems and intelligent energy management.

Publication Profile

Scopus | ORCID

Featured Publications

Moon, J.-H., Kim, J.-H., & Lee, J.-H. (2025). Sensor-Level Anomaly Detection in DC–DC Buck Converters with a Physics-Informed LSTM: DSP-Based Validation of Detection and a Simulation Study of CI-Guided Deception. Applied Sciences.

Moon, J., Lim, S., Kim, J., Kang, G., & Kim, B. (2024). A Study on the Mechanical Resonance Frequency of a Piezo Element: Analysis of Resonance Characteristics and Frequency Estimation Using a Long Short-Term Memory Model. Applied Sciences.

Moon, J., Park, S., & Lim, S. (2022). A Novel High-Speed Resonant Frequency Tracking Method Using Transient Characteristics in a Piezoelectric Transducer. Sensors.

Moon, J. H. (2021). A Study on Resonance Tracking Method of Ultrasonic Welding Machine Inverter. Journal of the Korean Society of Industry Convergence.

Moon, J. H. (2021). Fast and Stable Synchronization Between the Grid and Generator by Virtual Coordinates and Feed-Forward Compensation in Grid-Tied Uninterruptible Power Supply System. IEEE Access.

Assoc. Prof. Dr. CHEN CAO | electric engineering | Best Researcher Award

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Assoc. Prof. Dr. CHEN CAO | electric engineering | Best Researcher Award

Shenyang University of Technology, China

Dr. Cao Chen is an esteemed Associate Professor at the School of Electrical Engineering, Shenyang University of Technology. He serves as a Doctoral and Master Supervisor, contributing significantly to electrical engineering research and education. With expertise in power transformers, switchgear, and electrical equipment diagnostics, he has led multiple national and provincial-level research projects, published over 50 research papers, and holds 30 authorized patents. His contributions to science and technology have earned him numerous prestigious awards, including multiple first prizes in scientific and technological progress from the Liaoning Provincial Government and China Machinery Industry Federation.

Publication Profile

Scopus

🎓 Education & Experience:

Dr. Cao Chen completed his postdoctoral research at Shenyang University of Technology between 2018 and 2020. He then transitioned to academia, first as a Lecturer (2020-2021) and later as an Associate Professor (2021-present) at the School of Electrical Engineering, Shenyang University of Technology. His extensive teaching portfolio includes undergraduate and postgraduate courses on electrical engineering, switchgear disconnection technology, and energy and power engineering.

🏆 Awards and Honors:

Dr. Cao Chen has received numerous prestigious awards, including the Liaoning Provincial Science and Technology Progress Second Prize (2024, First Completer) and First Prize (2019) for advancements in transformer performance enhancement and switchgear flexibility. Additionally, he has won China Machinery Industry Science and Technology First Prizes (2016, 2024) for his pioneering work in high-voltage switching and overvoltage suppression technologies. He is also actively involved in IEEE PES Power Interruption Technical Committee (China) and multiple committees of the China Electrotechnical Society.

🔬 Research Focus:

Dr. Cao Chen’s research revolves around transformer diagnostics, power equipment reliability, and fault detection. His work focuses on developing advanced online monitoring and fault diagnosis techniques for power transformers using vibration-based methods and multi-source data fusion. He has spearheaded multiple projects funded by the National Natural Science Foundation of China, provincial departments, and corporate enterprises, driving innovation in power system reliability.

🔚 Conclusion:

Dr. Cao Chen is a pioneering researcher and educator in electrical engineering, particularly in power equipment diagnostics and fault analysis. His groundbreaking work in transformer monitoring, fault detection, and power system reliability has earned him national and international recognition. As a leader in scientific research, a dedicated professor, and a key contributor to industrial advancements, he continues to push the boundaries of electrical engineering through innovative solutions and impactful research. 🚀

📖 Publications:

State Diagnosis Method of Transformer Winding Deformation Based on Fusing Vibration and Reactance Parameters. IET Electric Power Applications. (Cited in SCI Zone 2)

Research on Simulation Analysis and Joint Diagnosis Algorithm of Transformer Core-Loosening Faults Based on Vibration Characteristics. Energies. (Read here)

Transformer winding mechanical state diagnosis method based on current-frequency-vibration parameters. Journal of Electric Machines and Control.

Magnetostrictive vibration model and loose fault diagnosis method of transformer core based on elastic mechanics-thermodynamics. High Voltage Technology.

Monitoring Method on Loosened State and Deformational Fault of Transformer Winding Based on Vibration and Reactance Information. IEEE ACCESS. (Cited in SCI Zone 1)

IDRISS DAGAL | Electrical Engineering | Best Researcher Award

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Dr. IDRISS DAGAL | Electrical Engineering | Best Researcher Award

Assistant Professor, Istanbul Beykent University, Turkey

Dr. Idriss Dagal, an Assistant Professor at Istanbul Beykent University, is a researcher and engineer from Chad specializing in Electrical Engineering, Renewable Energy, and Artificial Intelligence. With a career spanning over a decade, he has worked in various roles, including Aircraft Engineer and Lecturer, and has contributed extensively to the field of electrical systems, power electronics, and optimization algorithms. His academic journey includes a Ph.D. from Yıldız Technical University, Istanbul, Turkey, where he also completed his MSc in Avionics Engineering. Dr. Dagal has authored over 30 publications and is an active reviewer for renowned journals. 🌍💡

Publication Profile

ORCID

Education:

Dr. Dagal holds a Bachelor of Science (B.S.) degree in Industrial and Maintenance Engineering from Mongo Polytechnic University (Chad, 2006), a Master of Science (M.Sc.) in Aviation Engineering from Ethiopian Airlines Aviation School (Ethiopia, 2010), and a Ph.D. in Electrical Engineering from Yıldız Technical University (Turkey, 2022). He is currently pursuing a second M.Sc. in Avionics Engineering at Yıldız Technical University. 🎓📚

Experience:

Dr. Dagal’s professional experience spans multiple countries and roles, including serving as an Aircraft Maintenance Engineer in Chad, a Lecturer at various institutions in Chad, and a Sales Engineer in Turkey. Since 2024, he has been serving as an Assistant Professor at Istanbul Beykent University, Turkey, specializing in electrical engineering, renewable energy, and avionics. 🛠️✈️

Awards and Honors:

Dr. Dagal has received several prestigious awards, including the Chad’s Government National Scholarship (2003), Ethiopian Airlines Aviation School International Scholarship (2008), Turkish Government International Scholarship (2015), Young Research Scholarship Award for Eurasia Research (2019), and the Leadership Skills African Civic Engagement Academy (2022). 🏆🌟

Research Focus:

Dr. Dagal’s research interests are centered on optimization algorithms, artificial intelligence, renewable energy systems, power electronics, and aircraft control systems. His doctoral research focused on optimizing photovoltaic battery charging systems using hybrid particle swarm-based algorithms. He has a strong background in developing control mechanisms for sustainable energy systems and dynamic systems in aviation. 🔋🔧🚀

Conclusion:

Dr. Idriss Dagal is an accomplished academic and researcher who combines his expertise in electrical and aerospace engineering with a deep commitment to renewable energy and technology optimization. His interdisciplinary work continues to contribute to advancements in energy systems, aircraft control, and smart technologies. 🌱💻

Publications 

Energy transfer from PV panel to Battery via Buck-Boost Converter, International Journal of Technology and Science, Vol. 5, Issue 3, pp. 46-60, 26 November 2019. DOI

Improved salp swarm algorithm based on particle swarm optimization for maximum power point tracking of optimal photovoltaic systems, International Journal of Energy Research, 2022; 1-18. DOI: 10.1002/er.7753. Impact Factor: 4.3, Q1.

MPPT mechanism based on novel hybrid particle swarm optimization and salp swarm optimization (PSOSSO) algorithm for battery charging through Simulink, Scientific Reports Journal, 2022; 12:2664. DOI. Impact Factor: 3.8, Q1.

A Novel Hybrid Series Salp Particle Swarm Optimization (SSPSO) for Standalone Battery Charging Applications, Ain Shams Engineering Journal, 2022; 13:10174. DOI. Impact Factor: 6, Q1.

Improved Particle Swarm Optimization based Buck-Boost converter (IPSOBBC) for Photovoltaic System Application, Recent Advances in Science & Engineering (RASE), 2022.

Transformer rail-tapped buck-boost converter design-based feedback controller for battery charging systems, Energy Storage Journal, 2022; e414, DOI. ESCI.

Secure and Optimized Satellite Image Sharing based on Chaotic eπ Map and Racah Moments” Expert Systems with Applications, Volume 236, February 2024, 121247, DOI: 10.1016/j.eswa.2023.121247. Impact Factor: 7.5, Q1.

Hybrid SSA-PSO-based intelligent direct sliding-mode control for extracting maximum photovoltaic output power and regulating the DC-bus voltage, International Journal of Hydrogen Energy, Volume 51, Part C, 2 January 2024, Pages 348-370, DOI. Impact Factor: 8.1, Q1.

An Improved Constant Current Step-based Grey Wolf Optimization Algorithm for Photovoltaic Systems, Journal of Intelligent & Fuzzy Systems, 2024, DOI. Impact Factor: 1.7, Q3.

A Modified Multi-Stepped Constant Current Based on Grey Wolf Algorithm for Photovoltaics Applications, Springer, Electrical Engineering, 2024, DOI. Impact Factor: 1.6, Q3.