Modeling And Control Of Ac Machine Using Matlab R Simulink
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Modeling and Control of AC Machine using MATLAB®/SIMULINK
This book introduces electrical machine modeling and control for electrical engineering and science to graduate, undergraduate students as well as researchers, who are working on modeling and control of electrical machines. It targets electrical engineering students who have no time to derive mathematical equations for electrical machines in particular induction machine (IM) and doubly fed induction machines (DFIM). The main focus is on the application of field oriented control technique to induction motor (IM) and doubly fed induction motor (DFIM) in details, and since the induction motors have many drawback using this technique, therefore the application of a nonlinear control technique (feedback linearization) is applied to a reduced order model of DFIM to enhance the performance of doubly fed induction motor. Features Serves as text book for electrical motor modeling, simulation and control; especially modeling of induction motor and doubly fed induction motor using different frame of references. Vector control (field oriented control) is given in more detailed, and is applied to induction motor. A nonlinear controller is applied to a reduced model of an doubly induction motor associated with a linear observer to estimate the unmeasured load torque, which is used to enhance the performance of the vector control to doubly fed induction motor. Access to the full MATLAB/SIMULINK blocks for simulation and control.
Modeling and Control of AC Machine Using Matlab(r)/Simulink
This book introduces electrical machine modeling and control for electrical engineering and science to graduate, undergraduate students as well as researchers, who are working on modeling and control of electrical machines. It targets electrical engineering students who have no time to derive mathematical equations for electrical machines in particular induction machine (IM) and doubly fed induction machines (DFIM). The main focus is on the application of field oriented control technique to induction motor (IM) and doubly fed induction motor (DFIM) in details, and since the induction motors have many drawback using this technique, therefore the application of a nonlinear control technique (feedback linearization) is applied to a reduced order model of DFIM to enhance the performance of doubly fed induction motor. Features Serves as text book for electrical motor modeling, simulation and control; especially modeling of induction motor and doubly fed induction motor using different frame of references. Vector control (field oriented control) is given in more detailed, and is applied to induction motor. A nonlinear controller is applied to a reduced model of an doubly induction motor associated with a linear observer to estimate the unmeasured load torque, which is used to enhance the performance of the vector control to doubly fed induction motor. Access to the full MATLAB/SIMULINK blocks for simulation and control.
Modeling and Simulation of Systems Using MATLAB and Simulink
Not only do modeling and simulation help provide a better understanding of how real-world systems function, they also enable us to predict system behavior before a system is actually built and analyze systems accurately under varying operating conditions. Modeling and Simulation of Systems Using MATLAB® and Simulink® provides comprehensive, state-of-the-art coverage of all the important aspects of modeling and simulating both physical and conceptual systems. Various real-life examples show how simulation plays a key role in understanding real-world systems. The author also explains how to effectively use MATLAB and Simulink software to successfully apply the modeling and simulation techniques presented. After introducing the underlying philosophy of systems, the book offers step-by-step procedures for modeling different types of systems using modeling techniques, such as the graph-theoretic approach, interpretive structural modeling, and system dynamics modeling. It then explores how simulation evolved from pre-computer days into the current science of today. The text also presents modern soft computing techniques, including artificial neural networks, fuzzy systems, and genetic algorithms, for modeling and simulating complex and nonlinear systems. The final chapter addresses discrete systems modeling. Preparing both undergraduate and graduate students for advanced modeling and simulation courses, this text helps them carry out effective simulation studies. In addition, graduate students should be able to comprehend and conduct simulation research after completing this book.