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|a 10.1007/978-1-4615-5451-6
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|a Wehenkel, Louis A.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 245 |
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|a Automatic Learning Techniques in Power Systems
|h [electronic resource] /
|c by Louis A. Wehenkel.
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| 250 |
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|a 1st ed. 1998.
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| 264 |
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|a New York, NY :
|b Springer US :
|b Imprint: Springer,
|c 1998.
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| 300 |
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|a XXIX, 280 p.
|b online resource.
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|a Power Electronics and Power Systems,
|x 2196-3185
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|a Springer eBook Collection
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|a 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.
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| 520 |
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|a 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.
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