Mohammad Babaie

Also published under:M. Babaie, Mohammbad Babaie

Affiliation

GRÉPCI, École de Technologie Supérieure, Université du Québec, Montréal, Canada

Topic

Power Quality,Model Predictive Control,Switching Frequency,Multilevel Converter,Control Loop,Grid Voltage,Artificial Neural Network,Dc-link Voltage,Grid Current,Power Electronics,Voltage Levels,Voltage Waveforms,Weighting Factor,Point Of Common Coupling,Predictive Control,Voltage Stress,Component Count,Control Objective,Cost Function,Current Reference,Dc-link Capacitor,Hidden Layer,Input Current,Intelligent Control,Lyapunov Stability Theory,Multilayer Perceptron,Neutral Point Clamped,Nonlinear Load,Operation Mode,PI Controller,Power Factor,Reactive Load,Simulation Results,Switching Loss,Active Rectifier,Alternating Current,Ancillary Services,Angular Velocity,Asymptotically Stable,Common-mode Voltage,Control Strategy,DC Voltage,Dc Capacitor,Dc Source,Electric Vehicles,Grid Code,Grid Side,Kirchhoff’s Current Law,Low Voltage,Neural Network,

Biography

Mohammad Babaie (Student Member, IEEE) was born in Dorud, Iran, in January 14, 1992. He received the B.Sc. degree in electronic engineering from the Sepahan Science and Technology Higher Education Institute, Isfahan, Iran, in 2013, and the M.Sc. degree in control engineering from the Babol Noshirvani University of Technology (NIT), Babol, Iran, in 2016. He is currently pursuing the Ph.D. degree in power electrical engineering with the École de Technologie Superieure (ÉTS), University of Quebec, Montreal, QC, Canada.
He is a member of the Groupe de Recherche en Électronique de Puissance et Commande Industrielle (GRÉPCI). He has authored or coauthored several journal and conference papers in the field of control and power electronics and holds five patents. His research interests include developing variable structure control theory, modeling and control of power electronic converters using robust, adaptive and intelligent control techniques, applications of the classical and metaheuristic optimization algorithms in the control theory and the power systems, developing ANN training strategies with application in the power systems, and real-time control based on the field-programmable gate array (FPGA) and 32-bit MCUs for power electronic converters.