Optimization of large  electrical salient pole synchronous generators

doi:10.1108/03321640110393932

Login

Welcome:
Guest

Search for:

Browse:

Resources:

Our site has recently been upgraded & the page you requested has moved, so we have redirected you to the new location. Please amend your bookmarks.

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

ISSN: 0332-1649

Online from: 1982

Subject Area: Electrical & Electronic Engineering

Content: Latest Issue | Latest Issue RSS | Previous Issues

Options: To add Favourites and Table of Contents Alerts please take a Emerald profile

Optimization of large electrical salient pole synchronous generators

Document Information:
Title:	Optimization of large electrical salient pole synchronous generators
Author(s):	Ivan Mandic, (Sveucilište u Zagrebu, Viša tehnicka škola Zagreb, Zagreb, Croatia), Milica Pužar, (Elektrotehnicki fakultet, Osijek, Croatia), Marijan Petrinic, (Koncar-Institut za elektrotehniku, Zagreb, Croatia)
Citation:	Ivan Mandic, Milica Pužar, Marijan Petrinic, (2001) "Optimization of large electrical salient pole synchronous generators", COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, Vol. 20 Iss: 3, pp.813 - 827
Keywords:	Electrical machines, Generators, Linearization, Non-linear, Optimization, Programming
Article type:	Technical paper
DOI:	10.1108/03321640110393932 (Permanent URL)
Publisher:	MCB UP Ltd
Abstract:	The designs of salient pole generators may differ considerably from one hydroelectric plant to another. Automatic optimization procedure is highly desirable, because the designer may have little experience with a similar machine. The presented approach defines the design space by 12 variables which have the largest influence on the goal function. The design space is constrained by a number of linear and nonlinear constraints. The optimization process is based on successive linearizations of the goal function and the nonlinear constraints followed by a simplex procedure. The process is highly effective because the goal function is heavily constrained, so the optimum is virtually always on the boundary of the feasibility region. The procedure has been tested on a number of earlier designs. The goal function could have been reduced on average by some 8 percent, had this software been available at the time of the design of these machines.