IEEE Standard for Synchrophasors for Power Systems

En el presente trabajo se describen los principales componentes del sistema WAMS y la medición fasorial en sistemas eléctricos; se refieren criterios para la ubicación de PMU y elementos asociados en el sistema eléctrico. Posteriormente se hace una descripción del sistema WAMS instalado en el sistema eléctrico de Ecuador y se describen las características funcionales de la aplicación (software) de WAMS disponible en el Centro Nacional de Control de Energía -CENACE. Seguidamente se aplican los conceptos de ingeniería en el análisis de la información obtenida con el sistema WAMS instalado, presentándose los primeros resultados. Finalmente, y con base a los análisis realizados, se establecen los principales elementos de diseño y estrategias para la implementación de un sistema WAMS generalizado en el Sistema Nacional Interconectado -SNI del Ecuador. This paper describes the WAMS architecture and its main components, and depicts some criteria for the location of the elements of WAMS. Subsequently, a description of the application (software) of WAMS available at Centro Nacional de Control de Energía-CENACE, is carried out. A technical analysis of the information obtained from the installed WAMS, as well as a presentation of the first results of the implementation of WAMS in the SNI of Ecuador, are also deployed in this document. Finally, based on the analysis performed, major design elements and strategies for implementation of a generalized WAMS in the SNI of Ecuador are identified.Palabras clave: Concentrador de datos fasoriales, estabilidad oscilatoria, fasor, modos de oscilación, sincrofasor, sistema de monitoreo de área extendida, unidad de medición fasorial.

show abstract

“…Este estándar se aplica a las mediciones sincrofasoriales utilizadas por los sistemas WAMS en condiciones normales de operación, ver [5].…”

Section: Estándar Ieee C37118unclassified

Implementación de un sistema de monitoreo de área extendida WAMS en el Sistema Nacional Interconectado del Ecuador SNI

Torre

Cepeda

Herrera

2013

ings

View full text Add to dashboard Cite

show abstract

“…Let's refer to these groups of lines as multiple lines. The groups of multiple lines in the system include lines (1,2), (3,4), (5,6), (7,8), (9,10,11), (12,13), and (14,15). The green square highlights the groups of multiple lines in the system.…”

Section: ) Probability Evolution In Timementioning

confidence: 99%

“…The measurement co-variance is set to the standard value of Ω = 4.84 × 10 −4 I and has units of degrees [14]. The PMUs sample at a frequency of f = 30 Hz and the nominal voltage frequency is 60 Hz.…”

Section: A 37 Bus Test Casementioning

confidence: 99%

Line Outage Localization Using Phasor Measurement Data in Transient State

García

Catanach

Wiel

et al. 2016

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

Abstract-This work introduces a statistical classifier that quickly locates line outages in a power system utilizing only time series phasor measurement data sampled during the system's transient response to the outage. The presented classifier is a linear multinomial regression model that is trained by solving a maximum likelihood optimization problem using synthetic data. The synthetic data is produced through dynamic simulations which are initialized by random samples of a forecast load/generation distribution. Real time computation of the proposed classifier is minimal and therefore the classifier is capable of locating a line outage before steady state is reached, allowing for quick corrective action in response to an outage. In addition, the output of the classifier fits into a statistical framework that is easily accessible. Specific line outages are identified as being difficult to localize and future improvements to the classifier are proposed.

show abstract

“…A number of numerical algorithms for measuring phasor and power system frequency have been published in many literatures [3]- [11]. In general, the high speed measurement made within one or two cycles by applying short data window tends to have greater errors than those techniques using a long data window.…”

Section: Introductionmentioning

confidence: 99%

New Adaptive Linear Combination Structure for Tracking/Estimating Phasor and Frequency of Power System

Choowong-Wattanasakpubal¹,

Teratum-Bunyagul²

2010

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-This paper presents new Adaptive Linear Combination Structure (ADALINE) for tracking/estimating voltage-current phasor and frequency of power system. To estimate the phasors and frequency from sampled data, the algorithm assumes that orthogonal coefficients and speed of angular frequency of power system are unknown parameters. With adequate sampled data, the estimation problem can be considered as a linear weighted least squares (LMS) problem. In addition to determining the phasors (orthogonal coefficients), the procedure estimates the power system frequency. The main algorithm is verified through a computer simulation and data from field. The proposed algorithm is tested with transient and dynamic behaviors during power swing, a step change of frequency upon islanding of small generators and disconnection of load. The algorithm shows a very high accuracy, robustness, fast response time and adaptive performance over a wide range of frequency, from 10 to 2000 Hz.

show abstract

IEEE Standard for Synchrophasors for Power Systems

Cited by 202 publications

References 1 publication

Implementación de un sistema de monitoreo de área extendida WAMS en el Sistema Nacional Interconectado del Ecuador SNI

Implementación de un sistema de monitoreo de área extendida WAMS en el Sistema Nacional Interconectado del Ecuador SNI

Line Outage Localization Using Phasor Measurement Data in Transient State

New Adaptive Linear Combination Structure for Tracking/Estimating Phasor and Frequency of Power System

Contact Info

Product

Resources

About