By Louis A. Wehenkel
Automated studying is a fancy, multidisciplinary box of study and improvement, concerning theoretical and utilized equipment from records, laptop technological know-how, man made intelligence, biology and psychology. Its functions to engineering difficulties, corresponding to these encountered in electrical energy structures, are as a result demanding, whereas super promising. progressively more facts became on hand, gathered from the sector by way of systematic archiving, or generated via computer-based simulation. to address this explosion of information, computerized studying can be utilized to supply systematic techniques, with out which the expanding facts quantities and computing device strength will be of little use.
Automatic studying thoughts in energy Systems is devoted to the sensible software of automated studying to energy platforms. chronic structures to which automated studying may be utilized are screened and the complementary elements of computerized studying, with appreciate to analytical tools and numerical simulation, are investigated.
This publication offers a consultant subset of automated studying tools - easy and extra subtle ones - on hand from facts (both classical and modern), and from synthetic intelligence (both tough and gentle computing). The textual content additionally discusses applicable methodologies for combining those how you can make the simplest use of accessible info within the context of real-life difficulties.
Automatic studying recommendations in energy Systems is an invaluable reference resource for pros and researchers constructing computerized studying platforms within the electricity box.
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Additional info for Automatic Learning Techniques in Power Systems
Yannick Jacquemart provided part of the material presented in Chapter 10. MLP simulations were carried out with SIRENE, developed at the University of Liege by Michel Fombellida. Kohonen feature maps were based on SOMYAK, developed at the University of Helsinki by the team of Teuvo Kohonen. I thank both of them for making their software available. Part of Chapter 1 and Part II are adapted from a Tutorial course I gave with Yannick Jacquemart at the IEEE Power Industry Computer Application Conference, in May 1997.
To develop the top node each candidate attribute (here Pu and Qu) is considered in turn, in order to determine an appropriate threshold. To this end, the learning set is sorted by increasing order of the considered attribute values, then for each successive attribute value a dichotomic test is formulated and the method determines how well this test separates secure and insecure states, using an information theoretic score measure. 'rr... ,.. ll'- (b) Tesl 1>1 • ,. * . 4. 2 Successor 3 Successor 4 (d) Tree afler lhe firsl successor was developed Three first steps of decision tree growing (perfect separation).
I would like to encourage power system engineers to look at automatic learning in a sufficiently broad perspective, including methods from classical statistics, symbolic artificial intelligence and neural networks. The title suggests that many of the techniques and methodologies discussed in this book can be carried over to these other applications. Outline of the book The book is organized into an introduction and three parts. The Introduction first considers automatic learning and its application to power systems from a historical point of view, then provides an intuitive introduction to supervised and unsupervised learning.