Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Published on by Electrical Engineering

Ms. Xiaohua Li | Machine Learning | Excellence in Research Award

Associate Professor | Shanghai Electric Power University | China

Dr. Li Xiaohua, a distinguished Professor at Sichuan University and leading expert in materials science and structural engineering, is renowned for advancing high-performance composite materials and sustainable structural systems. She holds advanced degrees in materials engineering with specialization in composite behavior and structural performance, complemented by extensive experience in academic leadership, project supervision, and collaborative research initiatives. Her professional portfolio includes directing major institutional projects, mentoring interdisciplinary teams, and contributing to engineering innovations that strengthen the reliability and resilience of modern structures. Dr. Li’s research focuses on composite structures, fire-resistant materials, mechanical behavior, and performance optimization, supported by 297 citations, 34 scholarly documents, and an h-index of 11, reflecting her growing global impact. She has authored influential publications, contributed to high-level research panels, and advanced knowledge dissemination through editorial responsibilities and membership in professional engineering societies. Recognized for excellence in research, innovation, and service, she also holds relevant professional certifications that underscore her commitment to scientific rigor and continued advancement in the engineering sciences.

Profile: Scopus

Featured Publications

Li Xiaohua*, Probabilistic forecasting of coal consumption for power plants under deep peak shaving conditions using Informer with DDPM-based uncertainty modeling. Int. J. Electr. Power Energy Syst., 2025.

Li Xiaohua*, Electromagnetic vibration characteristics of permanent magnet synchronous motors with segmented grain-oriented electrical steel teeth–yoke.

Li Xiaohua, Research on core loss prediction of low-frequency transformer based on Grey Wolf optimisation algorithm optimised Back Propagation neural network. IET Electr. Power Appl., 2025.

 










































































 




	


	

	






	
	

		


Assoc. Prof. Dr. Guanlong Jia | Electric Engineering | Research Excellence Award
			

				Published on  by Electrical Engineering			

		
	


	

		



	
Assoc. Prof. Dr. Guanlong Jia | Electric Engineering | Research Excellence Award


Associate Professor | Hebei University of Technology | China
















Guanlong Jia, Lecturer at Hebei University of Technology and a Member of IEEE, is a researcher specializing in high-power electronics with expertise in circuit breakers, multilevel converters, control algorithms, and pulse-width modulation techniques. He holds a Ph.D. in electrical engineering from Zhejiang University, where he focused on advanced power electronic systems and their reliability. In his professional capacity, he contributes to teaching and research in power conversion technologies, participating in institutional and collaborative projects that enhance innovation in electrical engineering. His research centers on the design, analysis, and optimization of high-power electronic devices, and his contributions are reflected in his scholarly publications and technical advancements in power electronics. He is recognized for his academic engagement and his role in supporting the wider research community through professional membership and ongoing contributions to the field. At the end of his academic profile: 295 citations, 42 documents, and an h-index of 7.












Profile: Scopus


Featured Publications


Jia, Guanlong*, Transient stability enhancement method for virtual synchronous generators using power-angle deviation with a modified reactive-power control loop. Electronics (Switzerland), Accepted.


Jia, Guanlong*, Multi-objective optimization design of fast vacuum switch operating mechanisms for hydrogen-storage power systems. AIP Advances, Accepted.


Jia, Guanlong, Dynamics simulation and fault-characteristic analysis of permanent-magnet repulsion mechanisms for vacuum circuit breakers integrating advanced high-power switching technologies. AIP Advances, Accepted.


















	


	

	






	
	

		


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

				Published on  by Electrical Engineering			

		
	


	

		



	
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.