Prof. Sukumar Brahma, IEEE Fellow
Clemson University, USA
Sukumar Brahma received his Bachelor of Engineering from Gujarat University in 1989, Master of Technology from Indian Institute of Technology, Bombay in 1997, and PhD in from Clemson University in 2003; all in Electrical Engineering. He joined Clemson university as the Dominion Energy Distinguished Professor of Power Engineering in August 2018. He also serves as the director of the industry-funded Clemson University Electric Power Research Association (CUEPRA). Before joining Clemson he was William Kersting Endowed Chair Professor at New Mexico State University, USA. Dr. Brahma has chaired IEEE Power and Energy Society's Power and Energy Education Committee, Life Long Learning Subcommittee and Distribution System Analysis Subcommittee. He is a member of the Power System Relaying and Control Committee (PSRCC), where he has been contributing to and leading working groups that produce reports, guides and standards in the area of power system protection. He has been an editor for IEEE Transactions on Power Delivery, and served as Guest Editor-in-Chief for the Special Issue on Frontiers of Power System Protection for the journal. His research, widely published and funded by the National Science Foundation, US Department of Energy, utilities, and other government agencies has focused on different aspects of power system modeling, analysis, and protection. Fundamentally, it spans across diverse areas of electrical engineering and computer science, integrating signal processing, machine learning, control and communication to holistically approach the emerging problems in the power and energy domain. Current research, funded by the US Department of Energy, investigates and addresses protection and fault location issues in integration of renewables with power systems and develops new paradigms in protection of smart grid, at both transmission and distribution levels. Dr. Brahma is a Distinguished Lecturer of the IEEE. He was elected IEEE Fellow "for contributions to power system protection with distributed and renewable generation”.
IEEE PES Thailand Chapter
Asian Institute of Technology, Thailand
Prof. Dr. Weerakorn Ongsakul, CFA, ERP obtained B.Eng. (Electrical Eng.) in 1988 from Chulalongkorn University, Thailand; M.S. and Ph.D. (Electrical Eng.) from Texas A&M University, USA in 1991 and 1994, respectively. He is currently a Full Professor of Energy, Dept of Energy, Environment and Climate Change Asian Institute of Technology (AIT). He served as a Dean of School of Environment, Resources and Development, AIT from September 2009 to June 2013. His research encompasses the areas of Intelligent System Applications to Energy Systems, Power System Restructuring and Deregulation, and Energy Risk & Financial Risk Management. He has conducted national and international sponsored projects with a combined funding of US$30 million. Based on his research work, he has published more than 250 international refereed journal articles and conference proceedings papers. He is currently serving as Executive Director of Bangchak Innitiative and Innovation Center@AIT (BIIC@AIT), Secretary General of the Greater Mekong Subregion Academic and Research Network (GMSARN), Editor-in-Chief of GMSARN International Journal (Indexed by SCOPUS). He co-authored one book entitled Artificial Intelligence in Power System Optimization, CRC Press/Taylor & Francis in March 2013. He also received a number of national & international awards and recognitions which include amongst others, the Most Noble Order of the Crown of Thailand (Fifth Class) in 2008, the Most Exalted Order of the White Elephant (Fifth Class) in 2010, and the Royal Decoration on Companion (Seventh Class) of the Most Admirable Order of the Direkgunakorn bestowed by H.M. the King of Thailand in 2011, and Outstanding Engineer Award (OEA) 2019, IEEE PES Thailand Chapter, IEEE Power and Energy Society, USA. In addition, he has been a CFA Charterholder since Sept 2017and Certified Energy Risk Professional since March 2019.
Assoc Prof. Ng Yin Kwee
Nanyang Technological University, Singapore
Eddie is elected as:
Academician for European Academy of Sciences and Arts (EASA, EU);
Fellow of the American Society of Mechanical Engineers (FASME, USA);
Fellow of Institute of Engineering and Technology (FIET, United Kingdom);
Fellow of International Engineering and Technology Institute (FIETI, Hong Kong),
Distinguished Fellow for Institute of Data Science and Artificial Intelligence,
(DFIDSAI, China), and, Academician for Academy of Pedagogy and Learning, (USA).
He has published numerous papers in SCI-IF int. journal (430); int. conf. proceedings (130), textbook chapters (>105) and others (32) over the 29 years. Co-edited 14 books in STEM areas.
He is the: Lead Editor-in-Chief for the ISI Journal of Mechanics in Medicine and Biology for dissemination of original research in all fields of mechanics in medicine and biology since 2000;
Founding Editor-in-Chief for the ISI indexed Journal of Medical Imaging and Health Informatics (2011-2021);
Associate editor or EAB of various referred international journals such as Applied Intelligence, BioMedical Engineering OnLine, Computers in Biology & Medicine, and, Journal of Advanced Thermal Science Research.
He published > 542 papers in SCI-IF int. journal (430); int. conf. proceedings (130), textbook chapters (>105) and others (32) over the 27 years. Co-edited 14 books on “Cardiac Pumping and Perfusion Engineering” by WSP (2007); “Imaging and Modelling of Human Eye” by Artech (2008); “Distributed Diagnosis and Home Healthcare, v.1” by ASP (2009); “Performance Evaluation in Breast Imaging, Tumor Detection & Analysis” by ASP (2010); “Distributed Diagnosis and Home Healthcare, v.3” by ASP (2011); “Computational Analysis of Human eye with Applications” by WSP (2011); “Human Eye Imaging and Modeling” by CRC (2011); “Multimodality Breast Imaging” by SPIE (2013); “Image Analysis and Modeling in Ophthalmology”; “Ophthalmology Imaging and Applications” by CRC (2013, 2014); “Bio-inspired Surfaces and Applications” by WSP (2016); “Application of Infrared to Biomedical Sciences” by Springer (2017) and “Computation and Mathematical Methods in Cardiovascular Physiology” by WSP (2019). Also, co-authored a text book: “Compressor Instability with Integral Methods” by Springer (2007).
His main area of research is thermal
imaging, human physiology, biomedical engg; computational
turbomachinery aerodynamics; micro-scale cooling problems;
computational fluid dynamics & numerical heat transfer. As of
2020, he has graduated 24 Ph.D. and 28 M.Eng. research students.
One of my graduated PhD students, Dr. Saxena received the NTU’s
Graduate College Research Excellence Award (2020/21) and another
one, Dr Tan JH is listed as the Highly Cited Researchers by
Clarivate Web of Science in 2020.
More details can be found in: Cv: https://dr.ntu.edu.sg/cris/rp/rp00847
Speech Title: Machine Learning of
Wake Velocity and Turbulence Intensity of an Axial Wind Turbine
Speech Abstract: In this talk, three machine learning (ML) algorithms viz. Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Extreme Gradient Boosting (XGBoost) are implemented to predict wake velocity and turbulence intensity from a wind turbine at different downstream distances. To this end, a set of high-fidelity numerical simulations are performed for the NREL Phase VI wind turbine to produce training and test datasets for the three machine learning algorithms. Using the trained model, the wake flow field downstream of the blade and turbulence intensity are predicted on the test datasets which are hidden from the trained model. The prediction of wake velocity deficit and turbulence level in the wake from the machine learning algorithms are commensurate to the Computational Fluid Dynamics (CFD) simulations while running as fast as low-fidelity wake models. The wake velocity and turbulence intensity obtained from the ML models are also compared with some of the analytical wake models. The results reveal that machine learning-based algorithms can approximate wake and turbulence intensity characteristics better than the traditional analytical wake models.
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