Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Multi-machine power system state-space modelling for small-signal stability assessments

Ugalde Loo, Carlos Ernesto, Acha, Enrique and Licéaga-Castro, Eduardo 2013. Multi-machine power system state-space modelling for small-signal stability assessments. Applied Mathematical Modelling 37 (24) , pp. 10141-10161. 10.1016/j.apm.2013.05.047

Full text not available from this repository.

Abstract

This work presents a general state-space representation of a multi-machine, multi-order power system model, which may be used to carry out small-signal stability assessments. Computational software coded in MATLAB has been developed in order to find and analyse the solution of an arbitrary number of synchronous generators in the network. Each generator is represented by a pre-defined model. The model choice is tailored to fit the available data for each generator. The software has provisions for conducting power flow solutions and the calculation of the initial state that the generators keep prior to the disturbance. The state-space representation and the equivalent transfer function matrix of the system are generated automatically. Eigenvalue analysis may be carried out using the standard MATLAB functionality. The paper is one of a tutorial nature and in order to check on the sanity of the results given by the new software, two text-book networks have been examined. The results were also compared with those generated using commercial industrial-grade software.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Uncontrolled Keywords: State-space representation; Multi-machine systems; Mathematical modelling; Computational software
Publisher: Elsevier
ISSN: 0307-904X
Funders: CONACyT (Mexico)
Last Modified: 04 Jun 2017 05:06
URI: http://orca.cf.ac.uk/id/eprint/48473

Citation Data

Cited 5 times in Google Scholar. View in Google Scholar

Cited 8 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item