Non-linear regression model for inertia identification using synchrophasors and Big Data

Soto, Ismael

Keywords: inertia, power system, big data, PMU, synchrophasor

Abstract

This work proposes a model for inertia identification of an electrical power system. A non-linear regression is used from a mathematical model that relates power and frequency variation, obtained from a synchrophasor network. Fitting of the non-linear regressions processed with Big Data, from the PMU and the different real generation contingencies, obtained from the national system operator, are presented.

Más información

Título según WOS: A non-linear regression model for inertia identification using synchrophasors and Big Data
Título según SCOPUS: A non-linear regression model for inertia identification using synchrophasors and Big Data
Título de la Revista: 2021 IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021
Editorial: Institute of Electrical and Electronics Engineers Inc.
Fecha de publicación: 2021
Año de Inicio/Término: 22-26 March 2021
Idioma: English
Financiamiento/Sponsor: Project STICAMSUD 1l9-STIC-08 and DICYT 062117S.
URL: https://ieeexplore-ieee-org.ezproxy.usach.cl/document/9465263
DOI:

10.1109/ICAACCA51523.2021.9465263

Notas: ISI, SCOPUS