Artificial Neural Network Applications in Control of Induction Machines

Dinko Vukadinovic and Mateo Bašić
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University in Split, Split, Croatia

Series: Electrical Engineering Developments
BISAC: TEC000000

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Volume 10

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Special issue: Resilience in breaking the cycle of children’s environmental health disparities
Edited by I Leslie Rubin, Robert J Geller, Abby Mutic, Benjamin A Gitterman, Nathan Mutic, Wayne Garfinkel, Claire D Coles, Kurt Martinuzzi, and Joav Merrick

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Induction motors have been used as the workhorse in industry for a long time due to their being easy to build, highly robust, and having generally satisfactory efficiency. In addition, induction generators play an important role in renewable energy systems such as energy systems with variable-speed wind turbines. The induction machine is a nonlinear multivariable dynamic system with parameters that vary with temperature, frequency, saturation and operating point. Considering that neural networks are capable of handling time varying nonlinearities due to their own nonlinear nature, they are suitable for application in induction machine systems. In this book, the use of artificial neural networks for identification and control of induction machine systems is presented. This book presents a brief review of publications in this field according to the experience and estimates of the authors. (Imprint: Nova)

PREFACE

1. INTRODUCTION

2. DRIVE FEEDBACK SIGNAL ESTIMATION

3. INVERTER CONTROL

4. NEURAL NETWORK-BASED CONTROLLERS

5. IDENTIFICATION OF MACHINE PARAMETERS

6. NEURAL NETWORK BASED APPROACHES FOR THE EFFICIENCY IMPROVEMENT IN INDUCTION MACHINE SYSTEMS

7. NEURAL NETWORK IMPLEMENTATIONS BY DIGITAL SIGNAL PROCESSORS AND ASIC CHIPS

8. CONCLUSIO

REFERENCES

INDEX

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