Keynote Speech 2: Energy Conversion for a Sustainable Future--Revived Role of Power and Energy

Abstract

Energy sustainability is arguably one of the most critical challenges for a sustainable future. With predictions showing future scarcity and/or higher degree of extraction difficulty of traditional sources of energy for example coal, oil and natural gas, the shift to sustainable clean sources of energy is a must. Another key reason is the increasing detrimental impact of using fossil fuels. Over the last few decades, there has been serious effort to replace mechanical and hydraulic systems with electrical systems. This effort also includes replacing fixed-speed and old electrical drives with higher performance variable-speed drives. This is mainly due to the higher reliability, efficiency and robustness of electrical systems. This trend of “more electric” systems could be seen across a wide range of applications. These include traction, aerospace, actuation, mining, oil & gas, and industrial applications as examples. This push for electrification posed a lot of challenges to develop electrical systems that meet the demanding requirements of the various applications including harsh environments, high power density, high efficiency and fault tolerance in safety-critical applications. At the heart of the electrification effort is the development of advanced electrical machines and drives. This presentation will provide an overview of the various applications where electrification is taking place. The presentation will focus on electrical machines and drives that have been developed or are currently under development. The presentation will also cover some general trends in electrical machines and potential areas of research.

Biography

Ayman M. El-Refaie received the M.S. and Ph.D. degrees in electrical engineering from the University of Wisconsin Madison on 2002, and 2005 respectively. Between 2005 and 2016 he has been a principal engineer and a project leader at the Electrical Machines and Drives Lab at General Electric Global Research Center. His interests include electrical machines and drives. Since January 2017 he joined Marquette University as the Werner Endowed Chair for Energy Sustainability. He has over 200 journal and  conference publications. He has 48 issued US patents. He is currently serving as the IAS president-elect. He is an IEEE Fellow and a member of Sigma Xi.

Ayman M. El-Refaie

Werner Endowed Chair for Energy Sustainability, Marquette University

 

The information of the keynote speech(es) is listed below. More keynote speeches will be released later.

Abstract

Electrical equipment plays an important role in modern industrial applications. With the rapid development of electrification, the reliability of electrical equipment becomes more and more important. On-line monitoring can realize continuous condition monitoring of electrical equipment, provide early warning before the failure occurs, and avoid cascading reactions and greater economic losses caused by sudden failure. Non-intrusive monitoring method based on leakage current measuring will not have any impact on the normal operation of the original equipment, which provides great potential. This report discusses the theory and method of leakage current-based on-line monitoring of electrical equipment, including motors, transformers and cables. The ageing and failure mechanism of the electrical equipment will be discussed, together with the monitoring scheme and data processing. Taking the past research experience of the team in this area as an example, the report will show the technology of non-intrusive on-line monitoring of electrical equipment based on leakage current and the trend of electrical equipment health management.

Biography

Pin-Jia Zhang, associate professor, the Department of Electrical Engineering, Tsinghua University, senior member of the IEEE. He obtained his PhD from Georgia Tech, Atlanta, GA, USA in 2010. He was with the electric machines lab, GE Global Research from 2010 to 2015.

He is a member of the Standard Committee and Award Committee of IEEE Industrial Application Society, Secretary-General of Beijing Branch of IEEE Industrial Application Society. He serves as the associate editor IEEE Transactions on Industrial Electronics, IEEE Transactions on Industry Applications, and IEEE Access. He also serves as the convener of CIGRE/A1.45 on-line monitoring standards committee for large generator systems.

His research mainly focuses on the on-line monitoring and fault prognosis of electrical equipment. He has received 4 best paper awards by IEEE Industrial Application Society and Industrial Electronics Society. He has published over than 80 papers in refereed conference proceedings and journals, and has more than 40 patents granted. He is also the recipient of the 2018 IAS Andrew W. Smith Outstanding Young Member Achievement Award.

Pin-Jia Zhang

Associate professor, Tsinghua University

Keynote Speech 1: The Theory and Method of Non-Intrusive On-Line Monitoring of Electrical Equipment Based on Leakage Current Measuring

Abstract

The presentation focuses on pedagogic visualizations for graduate courses at the university and in more advanced research on the PhD level. As an example, 3D modelling is used to explain the design of winding end turns. Subsequently, a surface mounted PM motor is analyzed to investigate the field distribution and parameters. The philosophy is to see inside the machines to get more deep understanding of the functionality for advanced modelling to be developed later on. Salient pole synchronous machines are investigated to understand the concept of Xd and Xq reactances, reluctance torque and torque ripple. At the end, a fractional-slot PM machine with q<<1/2 is investigated to visualize the loss distribution which is dramatically influenced by the sub-harmonics which can be of the same order as the fundamental field in the machine.

The presentation is graphically oriented with pictures and animation using FEA and harmonic analysis using Matlab. 

Biography

Robert Nilssen received his Dr. -Ing. degree in 1989 from the Norwegian Institute of Technology (NTH). From 1989 to 1996 Nilssen worked at NTH as associate professor. In this period he was scientific advisor for SINTEF. Since 1996 Nilssen has been a professor at the Norwegian University of Science and Technology (NTNU) - with numerical electromagnetic field calculations as his main responsibility. In this period, Nilssen has participated in a series of research projects in which design and optimization have been important. Nilssen has also been the co-founder of several industrial companies. He has been scientific advisor for several companies, particularly for SmartMotor AS and Rolls-Royce Norway, focusing on marine and aviation applications of Permanent Magnet Machines.

Professor Nilssen has been a visiting professor at University of Minnesota (Minneapolis) in 1992, University of Wisconsin (Madison) in 2007, University of Sheffield in 2013 and Zhejiang University in 2019-2020.

Robert Nilssen

Professor, Norwegian University of Science and Technology

Keynote Speech 3: Numerical Visualization of Electrical Machine

Abstract

Physical fields (e.g. temperature, humidity, velocity, electric and magnetic fields, etc) are crucial information in many energy system design problems and they can be used to quantify the operating conditions and operating efficiency of these systems. Computational fluid dynamics (CFD) simulation is essentially the de facto tool to provide the dense information of these indoor physical fields but it requires extensive computation and good knowledge of boundary conditions. In this talk, we examine how sparse sensor observations can be efficiently fused with observed input parameters of CFD simulations for rapid reconstruction of new physical fields. From a physical field CFD database, we determine its principal modes through principal component analysis (PCA) and build a regression model between the input parameters and the PCA coefficients. This regression model allows us to construct an approximated physical field which can be updated with new sparse sensor observations. From the error analysis of the reconstruction process, it is noted that sensor placement is an important factor for accurate reconstruction. A novel eigen-based greedy algorithm is then developed to determine these sensing locations one-by-one until the required estimation accuracy is satisfied. Essentially, the selection of subsequent sensing location is based on the criterion that the observation vector has the maximum projection on the minimum eigenspace. This method is shown to be the most efficient in terms of computational complexity. Our Monte-Carlo simulations show that it outperforms the state of art sensor placement algorithms, especially when the number of sensors to be used is very limited.

Biography

Yeng Chai SOH received the B.Eng. degree in electrical and electronic engineering from the University of Canterbury, New Zealand, and the Ph.D. degree in control engineering from the University of Newcastle, Australia. He joined the Nanyang Technological University, Singapore after his PhD study, and is currently a professor in the School of Electrical and Electronic Engineering. Dr Soh has served as the Head of the Control and Instrumentation Division, the Associate Dean (Research and Graduate Studies) and the Associate Dean (Research) in the College of Engineering. He was also the founding director of NTU’s High Performance Computing Centre. Dr Soh has served in several national grants and scholarships evaluation and awards committees.

 

Dr Soh’s research interests are primarily in robust control and estimation, decentralized and distributed control and optimization, sensor networks and sensor fusion, AI and their applications in energy efficient systems. He has published more than 300 refereed journal papers in these areas and has received several international and national awards for his work. Dr Soh is a Fellow of the Academy of Engineering Singapore.

Yeng Chai SOH

Professor, Nanyang Technological University

Keynote Speech 4: Sensor Fusion and Placement for Physical Fields Reconstruction