IEEE Power & Energy Magazine - Spanish - January/February 2023 - 21

en el modelo del sistema y el operador del sistema no pudo tomar las medidas apropiadas para evitar el
apagón.
Una solución mejorada para
la gestión de datos erróneos y
la convergencia del estimador
de estado
En la comunidad, son conocidos los problemas de que los estimadores de estado no pueden garantizar
una solución ni rechazar datos erróneos; los investigadores e ingenieros lo denominan un problema
de robustez. El apagón del 2003 guió el enfoque de los ingenieros e investigadores del sistema eléctrico
hacia el hecho de que los estimadores
de estado aún podían mejorar. Se ha trabajado
en los últimos 20 años con el objetivo
de resolver el problema de la robustez, pero
no se han logrado avances significativos en
la práctica, más allá de mejoras progresivas.
Una encuesta sobre el menor rendimiento de
EMS, publicado por el Consejo de Confiabilidad
Eléctrica Nacional de Norteamérica
(NERC, por sus siglas en inglés) en 2020,
demostró que el 45 % de la pérdida de EMS
es causada por las fallas en los estimadores
de estado, lo que implica que los estimadores
de estado actuales siguen siendo vulnerables ante los datos erróneos en las mediciones, el modelo
del sistema y los parámetros.
En este artículo se analizan los problemas clave del estimador de estado estático del sistema eléctrico,
en especial
la
solución generalizada,
el algoritmo de mínimos
cuadrados ponderados (WLS, por
sus siglas en inglés). Se listan las
principales causas de la vulnerabilidad
de los estimadores de estado
de WLS. También se presenta
nuevo conocimiento sobre estos
problemas. Además, se señala que
las maneras actuales de lidiar con
las ponderaciones de las medidas
de inyecciones cero en los
centros de control de energía son
cuestionables, si bien se practican
actualmente en toda la industria de
la energía. Por último, el artículo
presenta un nuevo estimador de
estado que es completamente diferente
de los estimadores de estado
basados en WSL. Este nuevo estimador
de estado es el resultado de
varios proyectos patrocinados por
la Fundación Nacional de Ciencia
y dirigidos por los autores, entre
2013 y 2021. En este artículo, se
detallan los resultados numéricos
de una impresionante cantidad de
pruebas aleatorias que crean distintos
tipos de datos erróneos para
un sistema de prueba grande. Además,
se presentan los resultados de
pruebas en casos reales, proporcionados
por una empresa de energía
de América del Norte.
enero/febrero 2023
ieee power & energy magazine
21
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IEEE Power & Energy Magazine - Spanish - January/February 2023

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - Spanish - January/February 2023

Contents
IEEE Power & Energy Magazine - Spanish - January/February 2023 - Cover1
IEEE Power & Energy Magazine - Spanish - January/February 2023 - Cover2
IEEE Power & Energy Magazine - Spanish - January/February 2023 - Contents
IEEE Power & Energy Magazine - Spanish - January/February 2023 - 2
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IEEE Power & Energy Magazine - Spanish - January/February 2023 - Cover3
IEEE Power & Energy Magazine - Spanish - January/February 2023 - Cover4
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