Computational Methods for Electric Power Systems

Computational Methods for Electric Power Systems

by Mariesa L. Crow
Computational Methods for Electric Power Systems

Computational Methods for Electric Power Systems

by Mariesa L. Crow

Paperback(3rd ed.)

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Overview

Computational Methods for Electric Power Systems introduces computational methods that form the basis of many analytical studies in power systems. The book provides the background for a number of widely used algorithms that underlie several commercial software packages, linking concepts to power system applications. By understanding the theory behind many of the algorithms, the reader can make better use of the software and make more informed decisions (e.g., choice of integration method and step size in simulation packages).

This Third Edition contains new material on preconditioners for linear iterative methods, Broyden’s method, and Jacobian-free Newton–Krylov methods. It includes additional problems and examples, as well as updated examples on sparse lower-upper (LU) factorization. It also adds coverage of the eigensystem realization algorithm and the double-shift method for computing complex eigenvalues.


Product Details

ISBN-13: 9781032098227
Publisher: CRC Press
Publication date: 06/30/2021
Series: Electric Power Engineering Series
Edition description: 3rd ed.
Pages: 346
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Mariesa L. Crow is a professor of electrical engineering at the Missouri University of Science and Technology, Rolla, USA. Dr. Crow 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.

Table of Contents

Introduction. The Solution of Linear Systems. Systems of Nonlinear Equations. Sparse Matrix Solution Techniques. Numerical Integration. Optimization. Eigenvalue Problems.

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