2024 3rd International Conference on Smart Energy and Energy Internet of Things(SEEIoT 2024)
Keynote Speakers
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Prof. Zuqing Zhu, IEEE Fellow

University of Science and Technology of China

Zuqing Zhu received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of California, Davis, in 2007. From 2007 to 2011, he worked in the Service Provider Technology Group of Cisco Systems, San Jose, California, as a Senior Engineer. In January 2011, he joined the University of Science and Technology of China, where he currently is a Full Professor in the School of Information Science and Technology. He has published 360+ papers in peer-reviewed journals and conferences. He is the Steering Committee Chair of the IEEE International Conference on High Performance Switching and Routing (HPSR), and was the Chair of the Technical Committee on Optical Networking (ONTC) in IEEE Communications Society. He has received the Best Paper Awards from ICC 2013, GLOBECOM 2013, ICNC 2014, ICC 2015, and ONDM 2018. He is a Fellow of IEEE.

Title: Machine Learning in and for Optical Data-Center Networks 

Abstract: In the first part of this talk, we will first discuss the challenges on scalability, energy and manageability of data-center network (DCN) systems, and then explain why all-optical inter-connection can be a promising solution for future DCN systems. Next, we describe a novel all-optical inter-connection architecture based on arrayed waveguide grating router (AWGR) and wavelength-selective switches (WSS'), namely, Hyper-FleX-LION, explain its operation principle, and show experimental results of running distributed machine learning (DML) in a DCN in Hyper-FleX-LION. In the second part of this talk, we will explain how machine learning can be leveraged to realized knowledge-defined networking (KDN) and facilitate network automation in DCNs. Experimental results demonstrate that KDN can automatically reduce task completion time.



Prof. Shunli Wang, Academician of the Russian Academy of Natural Sciences

Smart Energy Storage Institute

Professor Shunli Wang is a Doctoral Supervisor, Academic Dean, Academic Leader of National Electrical Safety and Quality Testing Center (Sichuan), Academician of Russian Academy of Natural Sciences, Overseas Overseas High-level Overseas Educated Talent of Sichuan Province, Academic and Technical Leader of China Science and Technology City (Mianyang), and Selected in the List of Top 2% of Top Scientists in the World. Intelligent Energy Storage Research Institute, with new energy measurement and control research center, Sichuan Province power energy storage system safety operation and control engineering research center, Sichuan Province hydrogen energy and multi-energy complementary microgrid engineering technology research center, focusing on carrying out the smart grid low-carbon energy storage research, new energy and energy storage system key technology research and development and industrialization application. He has undertaken 56 projects of National Natural Science Foundation of China, Central Military Commission, Provincial Science and Technology Department, etc. He has published 173 articles in SCI-1 TOP and other journals, with Research Interest Score 11055, citations 2865, and h-index 27. 

Title:Smart Energy Storage System Safety Monitoring Technology and Application

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, an improved integrated algorithm based on multiple layer kernel extreme learning machine and genetic particle swarm optimization algorithm is 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 of LIB units are processed to construct the traditional high-dimensional health feature dataset and the low-dimensional fusion feature dataset, and each version of ML-ELM network is trained and tested separately. The numerical analysis of the prediction results shows that the root mean square error  of the comprehensive algorithm for SOH estimation is controlled within 0.66%. The results of the multi-indicator comparison show that the proposed algorithm 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.

Prof. Zhihua ZHANG, Deputy Dean of New Energy School

China University of Petroleum (East China)

Zhihua Zhang, Professor, PhD, Deputy Dean of New Energy School, China University of Petroleum (East China), Visiting Scholar, University of Manchester, UK. He is mainly engaged in research and teaching on protection and control of AC-DC hybrid power distribution system, optimization and regulation of multi-energy complementary integrated energy system, power quality monitoring, analysis and optimization governance, etc. It has undertaken more than 30 projects such as National Natural Science Foundation projects, National 863 major projects, National key research and development programs, Shandong Natural Science Foundation projects, Sinopec key science and technology research projects, and basic scientific research operational funds of central universities. He has published more than 50 academic papers, of which more than 30 are included in SCI/EI. More than 20 national invention patents have been applied for, and 10 have been authorized; Participated in the revision of the Power distribution and electricity volume of the China Power Encyclopedia, and participated in the formulation of 4 group standards of the China Power Supply Society; Won a number of provincial provincial and department level awards.



Prof. Yu Wang, IEEE Senior Member

Chongqing University, China 

Yu Wang received the B.Eng. degree in School of Electrical Engineering and Automation from Wuhan University, Wuhan China, and the M.Sc. and Ph.D. degree in Power Engineering from Nanyang Technological University, Singapore. Currently, he is a professor in Chongqing University. He was a Marie Skłodowska-Curie Individual Fellow with Control and Power Group, Department of Electrical and Electronic Engineering, Imperial College London. He has published more than 70 papers in quality journals and conference proceedings. He serves as associate editor of IET Generation, Transmission&Distribution, IET Renewable Power Generation, and secretary of 2 IEEE Task Forces. His research interests include microgrid control and stability, power system operation and control, and cyber-physical systems.