®®®® SIIA Público

Título del libro: 2019 Ieee Pes Conference On Innovative Smart Grid Technologies, Isgt Latin America 2019
Título del capítulo: Data-Driven Modal Features Extraction Through the Variational Mode Decomposition Method

Autores UNAM:
MARIO ROBERTO ARRIETA PATERNINA;
Autores externos:

Idioma:
Inglés
Año de publicación:
2019
Palabras clave:

Electric power transmission networks; Extraction; Mathematical transformations; Signal distortion; Smart power grids; Electromechanical modes; Hilbert transform; Mode decomposition; Power oscillations; Signal decomposition; Signal processing


Resumen:

This paper presents a low-frequency power system modal features extraction method for exploiting the Variational Mode Decomposition (VMD) method. The features extraction process is focused on a time-frequency analysis of nonlinear and non-stationary signals resulting from a large disturbance. The insight from using VMD is that the technique can accurately achieve signal decomposition in obtaining sub-band signals or modes from power oscillating signals. An optimal and recursive time-frequency formulation yields the modes and their instantaneous modal features such as amplitude, frequency, damping and energy, by applying Hilbert transform. The results demonstrate the applicability of the proposition in a large-scale power system. © 2019 IEEE.


Entidades citadas de la UNAM: