Distributed Voltage Security Monitoring in Large Power Systems Using Synchrophasors

This paper proposes a real-time distributed voltage security monitoring framework for large power systems using synchrophasor measurements. The method distributes its major computations among substations and collects results from each substation at the control center to provide a comprehensive volta...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on smart grid 2016-03, Vol.7 (2), p.982-991
Hauptverfasser: Xing Liu, Xun Zhang, Venkatasubramanian, Vaithianathan Mani
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper proposes a real-time distributed voltage security monitoring framework for large power systems using synchrophasor measurements. The method distributes its major computations among substations and collects results from each substation at the control center to provide a comprehensive voltage security index. At the substation level, two Q-V sensitivity-based voltage security indices, namely, a positive index and a negative index, are calculated from ambient synchrophasor measurements. The indices are estimated using two new algorithms, namely, fast bidirectional sensitivities calculation and bidirectional linearization approximation, using local measurements. These local sensitivities from different substations are collected at the control center level, where wide-area voltage security assessment is carried out to estimate the system proximity to voltage collapse, as well as to identify voltage insecure subareas if any exist. Simulations and numerical studies of the IEEE 118-bus test power system and validated large-scale model of the July 2, 1996 Western American blackout are used to illustrate that the proposed algorithm can detect voltage insecurity effectively in large power systems.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2015.2410219