Automatic Learning Techniques in Power Systems (Hardcover, 1998 ed.)

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.

General

Imprint:	Springer
Country of origin:	Netherlands
Series:	Power Electronics and Power Systems
Release date:	November 1997
First published:	1998
Authors:	Louis A. Wehenkel
Dimensions:	235 x 155 x 19mm (L x W x T)
Format:	Hardcover
Pages:	280
Edition:	1998 ed.
ISBN-13:	978-0-7923-8068-9
Categories:	Books > Professional & Technical > Energy technology & engineering > Electrical engineering > Power networks, systems, stations & plants
LSN:	0-7923-8068-1
Barcode:	9780792380689

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