Computational Methods for Electric Power Systems
2nd Edition
Published on 17. August 2009
304 pages
978-1-4200-8661-4 (ISBN)
Description
Improve Compensation Strategies for Package ShortcomingsIn today's deregulated environment, the nation's electric power network is forced to operate in a manner for which it was not designed. As a result, precision system analysis is essential to predict and continually update network operating status, estimate current power flows and bus voltages,
Reviews / Votes
"This is good resource material for a graduate student preparing for a qualifying exam"-- IEEE Power & Energy Magazine
More details
Series
Edition
2nd edition
Language
English
Place of publication
Baton Rouge
United States
Publishing group
Taylor & Francis Inc
Target group
College/higher education
Undergraduate and graduate students in software development, power engineering, power electronics, circuits and devices, and VLSI.
Edition type
Revised edition
Illustrations
85 s/w Abbildungen
85 b/w images and 700-800 equations
File size
2,75 MB
ISBN-13
978-1-4200-8661-4 (9781420086614)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Mariesa L. Crow
Computational Methods for Electric Power Systems, Second Edition
Book
08/2009
2nd Edition
CRC Press
€127.76
Article exhausted; check for reprint
Person
Dr. Mariesa Crow is a professor of electrical engineering at Missouri University of Science and Technology in Rolla. She is director of the Energy Research and Development Center. Her areas of research include: computer-aided analysis of power systems dynamics and security analysis, voltage stability, computational algorithms for analyzing stressed, non-linear, non-continuous systems. Power-electronic applications in bulk power systems (FACTS), and parameter estimation.
Content
Introduction. The Solution of Linear Systems. Systems of Nonlinear Equations. Sparse Matrix Solution Techniques. Numerical Integration. Optimization. Eigenvalue Problems.
