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.
Erratum (In preparation !)
GTDIDT (Download free demo software...)