Automatic Learning Techniques in Power Systems / Edition 1

by Louis A. Wehenkel

Overview

Automatic learning is a complex, multidisciplinary field of research and development, involving theoretical and applied methods from statistics, computer science, artificial intelligence, biology and psychology. Its applications to engineering problems, such as those encountered in electrical power systems, are therefore challenging, while extremely promising. More and more data have become available, collected from the field by systematic archiving, or generated through computer-based simulation. To handle this explosion of data, automatic learning can be used to provide systematic approaches, without which the increasing data amounts and computer power would be of little use.
Automatic Learning Techniques in Power Systems is dedicated to the practical application of automatic learning to power systems. Power systems to which automatic learning can be applied are screened and the complementary aspects of automatic learning, with respect to analytical methods and numerical simulation, are investigated.
This book presents a representative subset of automatic learning methods - basic and more sophisticated ones - available from statistics (both classical and modern), and from artificial intelligence (both hard and soft computing). The text also discusses appropriate methodologies for combining these methods to make the best use of available data in the context of real-life problems.
Automatic Learning Techniques in Power Systems is a useful reference source for professionals and researchers developing automatic learning systems in the electrical power field.

Product Details

ISBN-13:	9780792380689
Publisher:	Springer US
Publication date:	11/30/1997
Series:	Power Electronics and Power Systems
Edition description:	1998
Pages:	280
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

1. Introduction.- 1.1 Historical perspective on automatic learning.- 1.2 An automatic learning tool-box.- I Automatic Learning Methods.- 2. Automatic Learning is Searching a Model Space.- 3. Statistical Methods.- 4. Artificial Neural Networks.- 5. Machine Learning.- 6. Auxiliary Tools and Hybrid Techniques.- II Application of Automatic Learning to Security Assessment.- 7. Framework for Applying Automatic Learning to DSA.- 8. Overview of Security Problems.- 9. Security Information Data Bases.- 10. A Sample of Real-Life Applications.- 11. Added Value of Automatic Learning.- 12. Future Orientations.- III Automatic Learning Applications in Power Systems.- 13. Overview of Applications by Type.- References.

Editorial Reviews

Provides a historical, theoretical, and practical account of the application of automatic learning to power systems. Intended for those with a basic background in probability theory, linear algebra, calculus, and function optimization who are interested in developing applications of automatic learning to power systems, especially security assessment systems. Topics include statistical methods, artificial neural network based methods, machine learning, decision tree induction, and automatic learning algorithms. Includes extensive references and a glossary. Annotation c. by Book News, Inc., Portland, Or.

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