By Louis A. Wehenkel
Computerized studying is a posh, multidisciplinary box of learn and improvement, regarding theoretical and utilized tools from records, laptop technological know-how, man made intelligence, biology and psychology. Its functions to engineering difficulties, similar to these encountered in electricity structures, are accordingly demanding, whereas super promising. an increasing number of facts became to be had, gathered from the sector by means of systematic archiving, or generated via computer-based simulation. to deal with this explosion of knowledge, computerized studying can be utilized to supply systematic methods, with out which the expanding info quantities and desktop strength will be of little use.
Automatic studying concepts in strength Systems is devoted to the sensible program of computerized studying to strength structures. persistent platforms to which computerized studying may be utilized are screened and the complementary features of computerized studying, with admire to analytical equipment and numerical simulation, are investigated.
This booklet offers a consultant subset of computerized studying tools - simple and extra refined ones - to be had from facts (both classical and modern), and from man made intelligence (both difficult and tender computing). The textual content additionally discusses applicable methodologies for combining those the way to make the easiest use of obtainable facts within the context of real-life difficulties.
Automatic studying options in energy Systems is an invaluable reference resource for pros and researchers constructing automated studying structures within the electrical energy box.
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Extra info for Automatic Learning Techniques in Power Systems
In our example we use only two candidate attributes (Pu and Qu) since all other parameters are constant. 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.
This contributed significantly to formalize the application methodology and to develop software tools, able to handle real, large scale problems. It opened also new research directions and suggested new types of applications. Today, only a few large Utilities in North-America and Europe have actually assessed automatic learning based security assessment in the context of their specific power systems. At least one of them - Electricite de France - is presently using the methodology in real field studies.
The method starts with a list of attributes (also called candidate attributes) in terms of which it will formulate the tree tests. In our example we use only two candidate attributes (Pu and Qu) since all other parameters are constant. 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.