Migrating eprints.org data to a Fez repository

This document records the process of migrating eprints.org data to a Fez repository. Fez is a Web-based digital repository and workflow management system based on Fedora (http://www.fedora.info/). At the time of migration, the University of Queensland Library was using EPrints 2.2.1 [pepper] for its ePrintsUQ repository. Once we began to develop Fez, we did not upgrade to later versions of eprints.org software since we knew we would be migrating data from ePrintsUQ to the Fez-based UQ eSpace. Since this document records our experiences of migration from an earlier version of eprints.org, anyone seeking to migrate eprints.org data into a Fez repository might encounter some small differences. Moving UQ publication data from an eprints.org repository into a Fez repository (hereafter called UQ eSpace (http://espace.uq.edu.au/) was part of a plan to integrate metadata (and, in some cases, full texts) about all UQ research outputs, including theses, images, multimedia and datasets, in a single repository. This tied in with the plan to identify and capture the research output of a single institution, the main task of the eScholarshipUQ testbed for the Australian Partnership for Sustainable Repositories project (http://www.apsr.edu.au/). The migration could not occur at UQ until the functionality in Fez was at least equal to that of the existing ePrintsUQ repository. Accordingly, as Fez development occurred throughout 2006, a list of eprints.org functionality not currently supported in Fez was created so that programming of such development could be planned for and implemented.

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

Analysis of small signal stability margins using genetic optimization

Power system small signal stability analysis aims to explore different small signal stability conditions and controls, namely: (1) exploring the power system security domains and boundaries in the space of power system parameters of interest, including load flow feasibility, saddle node and Hopf bifurcation ones; (2) finding the maximum and minimum damping conditions; and (3) determining control actions to provide and increase small signal stability. These problems are presented in this paper as different modifications of a general optimization to a minimum/maximum, depending on the initial guesses of variables and numerical methods used. In the considered problems, all the extreme points are of interest. Additionally, there are difficulties with finding the derivatives of the objective functions with respect to parameters. Numerical computations of derivatives in traditional optimization procedures are time consuming. In this paper, we propose a new black-box genetic optimization technique for comprehensive small signal stability analysis, which can effectively cope with highly nonlinear objective functions with multiple minima and maxima, and derivatives that can not be expressed analytically. The optimization result can then be used to provide such important information such as system optimal control decision making, assessment of the maximum network's transmission capacity, etc. (C) 1998 Elsevier Science S.A. All rights reserved.