Beomju Kim | Power System Stability | Research Excellence Award

Published on by Electrical Engineering

Mr. Beomju Kim | Power System Stability | Research Excellence Award

Power System | Korea University | South Korea

Kim Beomju is a Ph.D. integrated program researcher in the Department of Electrical and Electronic Engineering at Korea University, specializing in power and energy systems. He holds a bachelor’s degree in electrical engineering and has developed strong expertise through advanced doctoral training focused on modern power grids. His professional experience includes active roles in nationally and industry funded projects in collaboration with major energy stakeholders, contributing to HVDC operation, offshore wind integration, grid robustness assessment, and system monitoring platforms. His research focuses on power system stability and dynamics, renewable energy integration, inertia estimation, and frequency stability, with publications in internationally indexed journals as well as patented technologies for advanced grid analysis and monitoring. His achievements include research excellence recognitions, patented innovations, and active membership in professional engineering societies. His scholarly impact is reflected in 10,746 citations, an h-index of 41, and an i10-index of 109.

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Featured Publications

Attack Vulnerability of Complex Networks
P. Holme, B. J. Kim, C. N. Yoon, S. K. Han
Physical Review E · Citations: 2564

Growing Scale-Free Networks with Tunable Clustering
P. Holme, B. J. Kim
Physical Review E · Citations: 1511

Synchronization on Small-World Networks
H. Hong, M. Y. Choi, B. J. Kim
arXiv Preprint · Citations: 573

Vertex Overload Breakdown in Evolving Networks
P. Holme, B. J. Kim
Physical Review E · Citations: 338

Factors That Predict Better Synchronizability on Complex Networks
H. Hong, B. J. Kim, M. Y. Choi, H. Park
Physical Review E · Citations: 314

Lei Guan | Machine Learning | Research Excellence Award

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Mr. Lei Guan | Machine Learning | Research Excellence Award

Director | China Academy of Safety Science and Technology | China

Lei Guan is a Director and Professor at the Risk Monitoring and Early Warning Center, China Academy of Safety Science and Technology, with expertise in risk monitoring, early warning systems, artificial intelligence, and industrial safety engineering. He holds a Bachelor’s degree in Materials Science and Master’s and Doctoral degrees in Mechanical Engineering with specialization in precision instruments and safety-related systems. He has led major national and ministerial research programs, directed key laboratories and professional committees, supervised graduate researchers, and provided technical leadership for large-scale industrial and governmental safety initiatives. His research focuses on intelligent work safety systems, industrial internet applications, digital twins, data-driven risk modeling, and emergency management, with sustained contributions through peer-reviewed publications, patents, and standards development. His scholarly impact is reflected in 18 citations, an h-index of 3, and 13 published articles.

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Featured Publications

Numerical simulation of the double pits stress concentration in a curved casing inner surface
W. Yan, L. Guan, Y. Xu, J.G. Deng – Advances in Mechanical Engineering, 9(1) (3 citations)

Safety monitoring and management system for fluid catalytic cracking (FCC) process
L. Fang, Z. Wu, L. Wei, R. Kang, L. Guan – International Conference on Information and Automation (3 citations)

Study on SVM-based Flame Recognition and Fire Warning for Cotton and Linen Warehouses
X. Zhao, S. Hao, L. Guan, Y. Wang, Q. Zhao, D. Lv – IEEE Conference on Advances in Electrical Engineering (2 citations)

Industrial Internet of Things (IIoT) Identity Resolution Techniques: A Review
C. Dai, H. Li, L. Guan, M. Chi – IEEE BigDataSecurity (1 citation)

Prof. Fazhi Song | Control Systems & Optimization | Research Excellence Award

Published on by Electrical Engineering

Prof. Fazhi Song | Control Systems & Optimization | Research Excellence Award

Professor | Harbin Institute of Technology | China

Dr. Fazhi Song, Professor in the School of Instrumentation Science and Engineering at Harbin Institute of Technology, is a leading specialist in control science and precision motion systems whose work advances high-end manufacturing and inspection technologies. With a Ph.D. in Control Science and Engineering and research expertise spanning motion generation, performance control, learning control, and system accuracy retentivity, he has built a distinguished academic and professional record through roles as researcher, lecturer, associate professor, and project leader on numerous advanced engineering projects. He has authored more than forty peer-reviewed publications, contributed a research monograph, and secured an extensive portfolio of patents and software copyrights, reflecting strong innovation and impact in precision motion control. His scholarly influence is further demonstrated by 432 citations across 367 documents, 45 indexed publications, and an h-index of 9. Dr. Song has been recognized with major honors, including high-level national and provincial awards for technological invention, innovation, and academic contribution, and he maintains active professional service as guest editor, editorial board member, conference session chair, peer reviewer for leading journals, and expert evaluator for national research programs. His contributions exhibit a blend of scientific rigor, technological advancement, and leadership, positioning him as an exemplary candidate for award recognition.

Profiles: Scopus | ORCID

Featured Publications

Fazhi Song, A compensation method for electromagnetic hysteresis: Application in linear reluctance actuator. J. Magn. Magn. Mater., 2025.*

Fazhi Song, Crest factor minimization of multisine signals based on the Chebyshev norm approximation method: With application to wafer stage FRF identification. Results Eng., 2025.*

Fazhi Song, Identification for precision mechatronics: An auxiliary model-based hierarchical refined instrumental variable algorithm. Int. J. Robust Nonlinear Control, 2025.*

Fazhi Song, Beyond performance of learning control subject to uncertainties and noise: A frequency-domain approach applied to wafer stages. IEEE/CAA J. Autom. Sinica, 2025, 5 citations.*

Dr. Angel Sapena Bano | Modelling Machines for Optimization | Research Excellence Award

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Dr. Angel Sapena Bano | Modelling Machines for Optimization | Research Excellence Award

Associate Professor | Universitat Politecnica de Valencia | Spain

Ángel Sapena Bañó, Profesor Titular at the Universitat Politècnica de València, is a specialist in electrical engineering with expertise in electrical machines, diagnostic methods, numerical modelling, and condition monitoring. He holds degrees in Industrial Engineering, Energy Technology for Sustainable Development, and Secondary Education, complemented by a doctorate in Industrial Engineering focused on advanced diagnostic techniques for electrical machines. His professional trajectory includes roles as Lecturer, Researcher, and Technical Specialist, contributing to major academic initiatives, laboratory modernization, and collaborative research activities. He has participated in multiple competitive and industrial R&D projects, developed fault-diagnosis tools for induction machines and wind-energy systems, and strengthened international cooperation through research stays and Erasmus teaching engagements. His research spans analytical and hybrid modelling, finite-element methods, machine-learning-based diagnostics, and real-time simulation, reflected in numerous high-impact journal articles, conference contributions, book chapters, and patented inventions. He has led and co-led research outputs as first and corresponding author, supervised a wide range of graduate projects, and contributed to organizing scientific conferences and special issues. His distinctions include recognized research merits, invited reviewer roles in indexed journals, participation in prominent research groups, and involvement in impactful national and international scientific initiatives. His scholarly record includes 1,035 citations, 60 documents, and an h-index of 17.

Profiles: Scopus | ORCID

Featured Publications

Ángel Sapena Bañó*, Model-based diagnostic techniques for induction machines under transient operational conditions. Int. J. Electr. Power Energy Syst., Accepted.

Ángel Sapena Bañó*, Hybrid FEM–analytical modelling framework for efficient fault detection in eccentric induction motors. Sensors, 2025, 25, 1–28.

Ángel Sapena Bañó, Deep learning–enhanced condition monitoring strategies for electrical machines operating in variable regimes. Mathematics and Computers in Simulation, 2025, 1–28.

Ms. Soujanya Reddy Annapareddy | BESS Power Flow and Energy | Energy Efficiency Excellence Award

Published on by Electrical Engineering

Ms. Soujanya Reddy Annapareddy | BESS Power Flow and Energy | Energy Efficiency Excellence Award

Senior Firmware Automation Engineer | TAE Power Solutions | United States

Ms. Soujanya Reddy Annapareddy is a Senior Firmware Automation Engineer at TAE Power Solutions, recognized for her expertise in embedded systems, firmware validation, and automation engineering. She holds advanced degrees in Computer Technology and Electronics and Communication Engineering, forming a strong interdisciplinary foundation that bridges software and hardware innovation. With extensive experience in developing Python-based automation frameworks, integration testing tools, and validation systems for complex embedded platforms, she has led and contributed to numerous industry and research projects aimed at enhancing automation efficiency and system reliability. Her research primarily focuses on firmware automation, embedded systems validation, IoT test architectures, software quality engineering, and cloud-integrated automation solutions. She has authored and co-authored more than twenty papers in international peer-reviewed journals and conferences, presenting impactful findings on energy-efficient embedded systems and adaptive control algorithms for battery energy storage systems. Beyond research, Ms. Annapareddy has served as a peer reviewer and judge for multiple reputed international journals, evaluating technical manuscripts across software automation and data-driven technologies. Her professional affiliations include membership in IEEE and active collaborations with multidisciplinary teams in academia and industry. She has been honored with recognitions for excellence in energy efficiency and innovation, reflecting her commitment to advancing intelligent, sustainable embedded technologies. Through her technical leadership, scholarly contributions, and dedication to continuous innovation, Ms. Soujanya Reddy Annapareddy exemplifies the qualities of a forward-thinking engineer and researcher in the global field of firmware automation and embedded systems engineering.

Profile: Google Scholar 

Featured Publications

Soujanya Reddy Annapareddy*, Managing power flows and energy efficiency in embedded systems for battery energy storage systems (BESS). Int. J. Adv. Innov. Dev. Res., Accepted.

Soujanya Reddy Annapareddy*, Python-based automation frameworks for firmware validation and integration testing in embedded platforms. Int. J. Innov. Res. Comput. Technol., 2024, 8(3), 245–252.

Soujanya Reddy Annapareddy, Adaptive algorithms for real-time power flow management in IoT-enabled systems. Int. J. Autom. Control Technol., 2024, 7(2), 118–129.

Soujanya Reddy Annapareddy, Machine learning-based predictive control for embedded energy storage systems. Int. J. Firmware Modern. Res., 2024, 6(4), 342–350.

Soujanya Reddy Annapareddy, Cloud-integrated automation systems for large-scale embedded firmware testing. Int. J. Latest Res. Publ., 2024, 5(1), 97–105.

Dr. Anas Kanaan | Cybersecurity in Electrical Systems | Best Researcher Award

Published on by Electrical Engineering

Dr. Anas Kanaan | Cybersecurity in Electrical Systems | Best Researcher Award

Assistant Professor | University of Petra | Jordan

Dr. Anas Ghassan Kanaan is an Assistant Professor in the Department of E-Business and Commerce at the University of Petra, Jordan, specializing in Management Information Systems with a focus on E-Business and Data Analytics. He holds a PhD in Management Information Systems (E-Business) and a Master’s in Information Technology from Universiti Utara Malaysia, along with a Bachelor’s degree in Computer Science from Mutah University. His academic and professional journey includes extensive teaching and leadership experience, serving as acting head of department and active member of numerous academic and strategic committees. Dr. Kanaan’s research primarily explores cybersecurity, business intelligence, e-government, and digital transformation, with multiple publications in Q1 and IEEE-indexed journals such as Behaviour & Information Technology and International Journal of Data and Network Science. His notable works address cybersecurity resilience, business continuity, and e-commerce innovation, contributing significantly to the advancement of smart business technologies. He has served as a peer reviewer for several international journals including Taylor & Francis and Elsevier, and has represented his institution at national workshops and conferences on digital infrastructure. His professional recognitions include the Distinguished Researcher Award from the University of Petra and multiple appreciation awards for academic and community service. Additionally, Dr. Kanaan is a certified professional trainer accredited by international organizations and actively contributes to academic excellence through curriculum development, research supervision, and technology-driven education. His research profile includes 117 citations across 102 documents, 12 publications, and an h-index of 6.

Profile: Google Scholar | ORCID | Scopus

Featured Publications

Anas Ghassan Kanaan*, Towards Business Continuity Management in the Saudi Healthcare Sector Through Security and Operational Risk Management. Emerald/Science Direct (Q1, Scopus).

Anas Ghassan Kanaan*, Optimizing E-Government Services through RFM Analysis and X-Means Clustering: A Data-Driven Approach to Citizen Segmentation and Service Personalization. Emerald (Q1, Scopus, Accepted).

Anas Ghassan Kanaan, Fortifying Organizational Cyber Resilience: An Integrated Framework for Business Continuity and Growth Amidst Escalating Threat Landscapes. Int. J. Comput. Digit. Syst., 16(1), 1–13 (Q3, Scopus).*

Anas Ghassan Kanaan, Cybersecurity Resilience for Business: A Comprehensive Model for Proactive Defense and Swift Recovery. IEEE Int. Conf. Cyber Resilience (ICCR).

Ms. Keenjhar Ayoob | Reliability Engineering | Best Researcher Award

Published on by Electrical Engineering

Ms. Keenjhar Ayoob | Reliability Engineering | Best Researcher Award

PhD Scholar | National university of sciences and technology | Pakistan

Dr. Keenjhar Ayoob is a PhD Scholar at the National University of Sciences and Technology (NUST), College of Electrical and Mechanical Engineering, specializing in Mechatronics and Robotics. He holds advanced degrees in Mechatronics Engineering with a focus on robotic systems and reliability engineering. His academic and professional experience includes research and collaboration with the National Center of Robotics and Automation (NCRA) and UESTC (China), where he has contributed to projects on robotic manipulator design, reliability modeling, and control optimization. Dr. Ayoob’s research centers on time-dependent reliability analysis, surrogate modeling, and intelligent optimization for enhancing the precision and torque efficiency of robotic systems. He has authored publications in SCI and Scopus-indexed journals including AIP Advances, PLOS ONE, and Engineering Proceedings (MDPI), and serves as a reviewer for the Journal of Mechanical Science and Technology (JMST). An IEEE Student Member, he is recognized for his innovative hybrid MRSM–GWO framework for torque optimization and Gaussian process-based learning models for adaptive robotic control. His ongoing work advances the integration of reliability engineering and machine learning to support adaptive and precise industrial automation applications.

Profile: ORCID

Featured Publications

Keenjhar Ayoob*, Reliability and torque optimization of robotic manipulators using hybrid MRSM–GWO framework. AIP Advances, Accepted.

Keenjhar Ayoob*, Surrogate modeling and intelligent optimization for adaptive trajectory control in robotic systems. PLOS ONE, Published.

Keenjhar Ayoob, Gaussian process-based learning models for time-dependent reliability analysis of robotic manipulators. Engineering Proceedings (MDPI), Published.

Dr. Cangbi Ding | Grid Integration of Renewable Energy | Best Academic Researcher Award

Published on by Electrical Engineering

Dr. Cangbi Ding | Grid Integration of Renewable Energy | Best Academic Researcher Award

Doctoral candidate at Nanjing University of Aeronautics and Astronautics, China

Cangbi Ding is a dedicated doctoral candidate at the Nanjing University of Aeronautics and Astronautics, specializing in the field of automation and power systems. With a solid academic foundation and a growing portfolio of impactful research, he has emerged as a promising scholar in the areas of high-voltage direct current (HVDC) systems, renewable energy integration, and power system stability control. His work is marked by innovation, technical rigor, and contributions that bridge both academic inquiry and industrial practice, positioning him as a strong candidate for recognition in international research awards.

Professional Profile

Scopus | Google Scholar

Education

Cangbi Ding began his academic journey by earning a Bachelor of Science degree in automation from Southeast University Chengxian College. He advanced his studies by pursuing a Master of Science degree at Nanjing University of Posts and Telecommunications, where he graduated with a focus on electrical engineering applications and automation technologies. Building upon this strong foundation, he is currently pursuing a Doctor of Philosophy degree in the College of Automation Engineering at the Nanjing University of Aeronautics and Astronautics. His doctoral research emphasizes planning and control methodologies for HVDC systems, a field that plays a crucial role in enhancing the efficiency and resilience of modern power grids. This progression highlights his commitment to advancing knowledge in the field of power and energy systems.

Experience

Throughout his academic career, Cangbi Ding has accumulated extensive experience through his involvement in numerous national and industrial research projects. He has actively participated in four projects funded by the National Natural Science Foundation of China, in addition to contributing to thirteen technological and consulting initiatives supported by the State Grid Corporation of China and the China Southern Power Grid Company. These experiences have equipped him with practical insights into large-scale energy systems while also refining his ability to bridge theory with real-world application. His consultancy roles in the energy sector reflect his capacity to provide innovative solutions for power system stability, grid modernization, and renewable energy integration. This blend of academic research and industrial collaboration illustrates his versatility and practical relevance as a researcher.

Research Focus

Cangbi Ding’s research centers on the integration of renewable energy into existing power grids, with particular emphasis on the planning and control of HVDC systems. His investigations into reactive power compensation after renewable energy integration address critical challenges in maintaining voltage stability and economic efficiency in power networks. By introducing both static and dynamic voltage stability indicators, he has proposed staged compensation methods that improve both system safety and cost-effectiveness. Another significant contribution is his work on HVDC system evolution, where he developed evaluation methods to classify systems and analyze their operational characteristics. His research not only advances academic understanding but also provides actionable methodologies for improving energy transmission infrastructure in the context of growing renewable energy adoption.

Awards & Honors

While still in the early stages of his academic career, Cangbi Ding has built a commendable record of achievements that reflect his innovation and technical acumen. His involvement in nationally significant projects, combined with his success in patenting technological innovations, underscores his contributions to the field. He has secured twelve granted invention patents and one utility model patent, an extraordinary accomplishment for a young researcher. These recognitions illustrate his capacity to transform theoretical advancements into practical innovations, positioning him as a standout contributor to the future of electrical engineering and power systems research.

Publication Top Notes

Title: Adaptive frequency control strategy for PMSG-based wind power plant considering releasable reserve power
Authors: J Dai, C Ding, X Zhou, Y Tang
Journal: Sustainability 14 (3), 1247

Title: Deep reinforcement learning-based voltage control method for distribution network with high penetration of renewable energy
Authors: S Liu, C Ding, Y Wang, Z Zhang, M Chu, M Wang
Journal: 2021 IEEE Sustainable Power and Energy Conference (iSPEC), 287-291

Title: Data-driven prediction of wind turbine blade icing
Authors: L Liu, D Guan, Y Wang, C Ding, M Wang, M Chu
Journal: 2021 China Automation Congress (CAC), 5211-5216

Title: Robust optimization method of power system multi resource reserve allocation considering wind power frequency regulation potential
Authors: J Dai, C Ding, C Yan, Y Tang, X Zhou, F Xue
Journal: International Journal of Electrical Power & Energy Systems 155, 109599

Title: An adaptive ufls scheme incorporating the impact of load response
Authors: W Zhu, C Ding, J Wu
Journal: 2021 IEEE Sustainable Power and Energy Conference (iSPEC), 2617-2622

Title: An active power coordination control strategy for AC/DC transmission systems to mitigate subsequent commutation failures in HVDC systems
Authors: X Zhou, C Ding, J Dai, Z Li, Y Hu, Z Qie, F Xue
Journal: Electronics 10 (23), 3044

Title: Research on the multi-timescale optimal voltage control method for distribution network based on a DQN-DDPG algorithm
Authors: M Ma, W Du, L Wang, C Ding, S Liu
Journal: Frontiers in Energy Research 10, 1097319

Title: Optimal Configuration Method for Multi-Type Reactive Power Compensation Devices in Regional Power Grid with High Proportion of Wind Power
Authors: Y Wang, J Dang, C Ding, C Zheng, Y Tang
Journal: Energy Engineering 121 (11)

Title: Research on the Optimal Configuration of Regional Integrated Energy System Based on Production Simulation
Authors: T Shi, RM Huang, CB Ding
Journal: Processes 8 (8), 892

Title: Research on The Fault Diagnosis Method of Oil-Immersed Transformers Based on The Improved DBSCAN Algorithm
Authors: W Cui, M Chu, C Ding, Y Wang, M Wang, L Liu
Journal: 2021 China Automation Congress (CAC), 5171-5176

Title: Voltage Interaction Evaluation in Embedded DC Transmission System
Authors: C Ding, C Zheng, Y Tang, C Zhang, X Han
Journal: Journal of Modern Power Systems and Clean Energy

Title: Cooperative Operation Control Strategy of Multi-Type Reactive Power Compensation Devices in Regional Power Grid with High Proportion of Wind Power
Authors: J Xu, Y Shen, K Li, X Wang, C Zheng, C Ding
Journal: 2024 6th International Conference on Electrical Engineering and Control

Title: Key Parameters Optimization Method of Wind Turbine Reactive Power Support Considering Power Angle Stability and Short-Circuit Current
Authors: J Xu, Y Shen, K Li, X Wang, C Zheng, C Ding
Journal: 2024 IEEE PES 16th Asia-Pacific Power and Energy Engineering Conference

Title: Coordinated CFPREV Control for Cascading Commutation Failure Mitigation in Multi-infeed HVDC Systems
Authors: J Xu, K Li, Y Shen, X Wang, C Zheng, C Ding
Journal: 2023 2nd Asia Power and Electrical Technology Conference (APET), 131-138

Title: Cooperative Reactive Power Configuration of Hybrid HVDC Transmission System for Offshore Wind Farm Clusters
Authors: Y Lin, Y Tang, W Wu, C Ding, C Zheng, Y Tang
Journal: 2023 IEEE 7th Conference on Energy Internet and Energy System Integration

Title: Research on Equivalent Modeling of Wind Farm Based on Error Correction Method
Authors: J Dai, C Ding, L Liu
Journal: 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI)

Title: Optimal Scheduling Analysis of Wind Farm Group Considering False Data Injection Attack
Authors: J Wu, C Ding, W Zhu
Journal: 2022 34th Chinese Control and Decision Conference (CCDC), 5433-5439

Title: Research on HVDC Subsequent Commutation Failure Suppression Strategy Considering Energy Storage Phase Modulation Operation
Authors: C Ding, W Zhu, S Liu, W Cui, Z Zhang, J Wu
Journal: 2021 IEEE Sustainable Power and Energy Conference (iSPEC), 1177-1182

Conclusion

Cangbi Ding’s academic journey and research accomplishments exemplify a blend of scholarly rigor, innovation, and practical relevance. With a foundation of strong academic training, significant project involvement, and a growing portfolio of publications and patents, he has already established himself as a promising researcher in electrical engineering. His focus on HVDC systems and renewable energy integration directly addresses pressing global challenges in energy sustainability and grid modernization. By translating complex theoretical concepts into practical solutions, he demonstrates the capacity to shape the future of power systems. His achievements make him an outstanding nominee for recognition in research excellence awards, reflecting his potential to continue contributing groundbreaking work to the advancement of electrical engineering.