ICSGCE Keynote Speakers

Keynote Speakers

Prof. Qinghua Wu

Life Fellow, IEEE; Fellow, IET; Fellow, AAIA; Fellow, CSEE;
South China University of Technology, China

Bio - Prof. Qinghua Wu received his Ph.D. from the Queen’ s University of Belfast in 1987, and subsequently spent over three decades in the UK. His research has primarily focused on power systems and intelligence engineering. Since 1995, he has served as a Chair Professor of Power Engineering at the University of Liverpool and Director of National Instruments e-Automation Laboratory, where he led a research team comprising 25 Ph.D. students and postdoctoral researchers. His extensive work in smart grid technologies has earned him numerous prestigious awards, including: Advanced Technology Award from National Semiconductor Corporation (USA), Outstanding Scientist Award from the UK Engineering and Physical Sciences Research Council (EPSRC), Best Paper Awards from the Institution of Mechanical Engineers (IMechE) and the Institute of Measurement and Control (InstMC), UK.

Prof. Wu joined South China University of Technology (SCUT) in 2011. He currently holds multiple positions: National Distinguished Professor, Director of the Energy Research Institute, SCUT, Discipline Leader in Electrical Engineering, SCUT School of Electric Power Engineering, Head of the Guangdong Province Overseas Innovation Team. Prof. Wu is a pioneer in artificial intelligence, and its applications in Smart Grid research. Professor Wu has published extensively in internationally renowned journals and conferences: 400+ journal papers in top-tier international journals, 350+ conference papers, 20 book chapters, 5 research monographs published by Springer, 30+ patents, 5 international patents. His work is among the most highly cited in the power systems field, consistently ranking among the world’s top scholars in citation impact. He has been recognized as a Highly Cited Researcher for many years. As of 2020, his paper are cited more than 2,200 times/per year, with a Google Scholar h-index of 77 and an i10-index of 380. He has managed and undertaken many research projects of the UK Engineering & Physical Sciences Research Council, National Natural and Science Foundation of China, National Basic Research Program of China (973 Program), Guangdong Innovative Research Team Program, British Telecom, Alstom Ltd, Siemens Ltd, National Grid, National Instruments, China Southern Power Grid, and he has been supported by a large amount of research funds, etc. He is Life Fellow of IEEE, Fellow of IET, the Executive Deputy Editor-in-Chief of the CSEE Journal of Power and Energy Systems, one of the first foreign Fellows of the CSEE, and Fellow of the AAIA.

Prof. Jianhua Zhang
North China Electric Power University, China

Bio - Prof. Zhang received her Bachelor, Master and PhD degree in North China Electrical Power University, the Graduate Department of North China Electric Power University in Beijing, and Beijing University of Aeronautics and Astronautics, respectively. She is now a Professor at North China Electric Power University, China. Her research interests mainly focus on modelling and control of advanced energy systems. She has published over 180 peer viewed journal/conferences papers. She is the principal investigator of several projects, including those funded by China’s National Basic Research Program (973 Program), the National Key R&D Program, five projects supported by the National Natural Science Foundation of China, and a project sponsored by the Newton Fund of the Royal Society. Her achievements have been recognized with several awards, including the second and third prizes in the Science and Technology Awards in Beijing, the second prize from the Ministry of Education of China’s Science and Technology Awards, the first prize from the China Simulation Society’s Science and Technology Awards, and the first prize from the China Instrumentation Society’s Science and Technology Awards.

Title of Speech: SFR Modeling and Short-term Frequency Regulation for Hybrid Power Systems

Abstract: With the energy transition towards carbon neutrality, high-penetration renewable energy sources will be connected to the power grid. This poses some new operation and generation challenges. Apart from the reduced system inertia, the stochastic fluctuations induced by photovoltaic (PV) power plants and wind power plants lead to the randomness and volatility of power generation. In this talk, we will introduce the background of Wind-PV-Thermal hybrid power systems (HPSs) and some existing work on system frequency modelling and frequency regulation methods of HPSs. We then present our recent work on modelling of HPSs by combining both physical model-based and data-driven modelling methods. Furthermore, we will discuss short-term frequency regulation strategies of HPSs by jointly utilizing wind power, photovoltaics and thermal power frequency regulation resources.

Prof. Hongfa (Henry) Hu
University of Windsor, Canada

Bio - Dr. Hongfa (Henry) Hu is a tenured full Professor at Department of Mechanical, Automotive & Materials Engineering, University of Windsor. He was a senior research engineer at Ryobi Die Casting (USA), and a Chief Metallurgist at Meridian Technologies, and a Research Scientist at Institute of Magnesium Technology.
He received degrees from University of Toronto (Ph.D., 1996), University of Windsor (M.A.Sc., 1991), and Shanghai University of Technology (B.A.Sc., 1985). He was a NSERC Industrial Research Fellow (1995-1997). His publications (over 200 papers) are in the area of magnesium alloys, composites, metal casting, computer modelling, and physical metallurgy. He was a Key Reader of the Board of Review of Metallurgical and Materials Transactions, a Committee Member of the Grant Evaluation Group for Natural Sciences and Engineering Research Council of Canada, National Science Foundation (USA) and Canadian Metallurgical Quarterly. He has served as a member or chairman of various committees for CIM-METSOC, AFS, and USCAR.
The applicant’s current research is on materials processing and evaluation of light alloys and composites. His recent fundamental research is focussed on transport phenomena and mechanisms of solidification, phase transformation and dissolution kinetics. His applied research has included development of magnesium automotive applications, cost-effective casting processes for novel composites, and control systems for casting processes. His work on light alloys and composites has attracted the attention of several automotive companies.

Title of Speech: Lightweight Materials for Potential Application in Automotive and Power Industries: An Overview

Abstract: Development of lightweight Al alloys with high electrical conductivities, tensile properties and low cost has become an urgent task for market expansion of battery-powered electric vehicles in the automotive industry and replacement of pure aluminum steel reinforced transmission lines in the power industry for the past few years. Manganese (Mn) is one of the alloying elements often used in wrought Al alloys such as AA3xxx series, which can ensure uniform deformation and enhance strengths, compared to pure aluminum. This is because Mn has a low maximum solubility of 1.25 wt% in Al under equilibrium solidification and forms Al6Mn dispersoid as a strengthening phase. In this paper, the effects of Mn on the electrical and mechanical properties of Al alloys are reviewed. The electric conduction principles and microstructure characteristics of Mn-containing Al alloys are presented. The strengthening mechanisms are discussed. The effects of cooling rates on the tensile properties of Mn-containing Al alloys are highlighted.

Prof. Shunli Wang

IET Fellow; Top 2% Worldwide Scientist
Inner Mongolia University of Technology, China

Bio - Prof. Shunli Wang is a Doctoral Supervisor, Executive Vice President of Smart Energy Storage Institute, Academic Dean of Electric Power College in Inner Mongolia University of Technology, Deputy General Manager of Daqingshan Laboratory in Inner Mongolia Electric Power Group, Visiting Professor of Xi 'an Jiaotong-Liverpool University, Academician of the Russian Academy of Natural Sciences, Academician of the Russian Academy of Engineering, IET Fellow, Academic Leader of National Electrical Safety & Quality Testing Center, Tianfu Qingcheng Scientific and Technological Talent, High-level Overseas Talent, Tianfu A Talents, Academic & Technical Leader of Chinese Science and Technology City, and Top 2% Worldwide Scientist. Focusing on the major national strategic needs of new energy and energy storage systems, the research of green and low-carbon energy storage is conducted in smart grids, undertaken 56 projects such as the National Natural Science Foundation and National Key Research & Development, with a Research Interest Score value of 13985, and 258 articles published on SCI-indexed famous journals with 52 articles in the First Area / TOP journals in Chinese Academy of Sciences, 39 high-cited / hot ones, 63 authorized invention patents and standards and 10 books. 9 awards have been achieved, including 3 international gold medals. 17 chairmanship of international conferences, and 5 editorial boards of international periodicals. The core technical achievements have reached the international advanced level and reported by People's Daily.

Title of Speech: Situation Awareness of Energy Storage Stations in Smart Grid

Abstract: As an important component of the smart grid energy storage system, high-precision state of health estimation of lithium-ion batteries is crucial for ensuring the power quality and supply capacity of the smart grid. To achieve this goal, improved integrated smart algorithms are proposed to estimate the SOH of Lithium-ion batteries. Kernel function parameters are used to simulate the update of particle position and speed, and genetic algorithm is introduced to select, cross and mutate particles. The improved particle swarm optimization is used to optimize the extreme value to improve prediction accuracy and model stability. The cycle data of different specifications are processed to construct the traditional high-dimensional health feature dataset and the low-dimensional fusion feature dataset, and each version of the constructed network is trained and tested separately. The results of the multi-indicator comparison show that the proposed algorithms can track the true value stably and accurately with satisfactory high accuracy and strong robustness, providing guarantees for the efficient and stable operation of the smart grid.