Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Meng Wang, Joe H. Chow, Denis Osipov, Stavros Konstantinopoulos, Shuai Zhang, Evangelos Farantatos, Mahendra Patel
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Synchrophasor data low rankness matrix completion tensor analysis adaptive filtering Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
Acceso en línea:	https://doaj.org/article/cdba7ef47afd4fdd879046d063c69ada
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:cdba7ef47afd4fdd879046d063c69ada
record_format	dspace
spelling	oai:doaj.org-article:cdba7ef47afd4fdd879046d063c69ada2021-11-10T00:09:25ZReview of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement2687-791010.1109/OAJPE.2021.3090579https://doaj.org/article/cdba7ef47afd4fdd879046d063c69ada2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9459703/https://doaj.org/toc/2687-7910There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.Meng WangJoe H. ChowDenis OsipovStavros KonstantinopoulosShuai ZhangEvangelos FarantatosMahendra PatelIEEEarticleSynchrophasor datalow ranknessmatrix completiontensor analysisadaptive filteringDistribution or transmission of electric powerTK3001-3521Production of electric energy or power. Powerplants. Central stationsTK1001-1841ENIEEE Open Access Journal of Power and Energy, Vol 8, Pp 532-542 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Synchrophasor data low rankness matrix completion tensor analysis adaptive filtering Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841
spellingShingle	Synchrophasor data low rankness matrix completion tensor analysis adaptive filtering Distribution or transmission of electric power TK3001-3521 Production of electric energy or power. Powerplants. Central stations TK1001-1841 Meng Wang Joe H. Chow Denis Osipov Stavros Konstantinopoulos Shuai Zhang Evangelos Farantatos Mahendra Patel Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
description	There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.
format	article
author	Meng Wang Joe H. Chow Denis Osipov Stavros Konstantinopoulos Shuai Zhang Evangelos Farantatos Mahendra Patel
author_facet	Meng Wang Joe H. Chow Denis Osipov Stavros Konstantinopoulos Shuai Zhang Evangelos Farantatos Mahendra Patel
author_sort	Meng Wang
title	Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
title_short	Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
title_full	Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
title_fullStr	Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
title_full_unstemmed	Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement
title_sort	review of low-rank data-driven methods applied to synchrophasor measurement
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cdba7ef47afd4fdd879046d063c69ada
work_keys_str_mv	AT mengwang reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement AT joehchow reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement AT denisosipov reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement AT stavroskonstantinopoulos reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement AT shuaizhang reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement AT evangelosfarantatos reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement AT mahendrapatel reviewoflowrankdatadrivenmethodsappliedtosynchrophasormeasurement
_version_	1718440788850900992

Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

Ejemplares similares