DocumentCode :
648349
Title :
Predicting critical transitions from time series synchrophasor data
Author :
Cotilla-Sanchez, Eduardo ; Hines, Paul ; Danforth, Christopher
fYear :
2013
fDate :
21-25 July 2013
Firstpage :
1
Lastpage :
1
Abstract :
The dynamical behavior of power systems under stress frequently deviates from the predictions of deterministic models. Model-free methods for detecting signs of excessive stress before instability occurs would therefore be valuable. The mathematical frameworks of “fast-slow systems” and “critical slowing down” can describe the statistical behavior of dynamical systems that are subjected to random perturbations as they approach points of instability. This paper builds from existing literature on fast-slow systems to provide evidence that time series data alone can be useful to estimate the temporal distance of a power system to a critical transition, such as voltage collapse. Our method is based on identifying evidence of critical slowing down in a single stream of synchronized phasor measurements. Results from a single machine, stochastic infinite bus model, a three machine/nine bus system and the Western North American disturbance of 10 August 1996 illustrate the utility of the proposed method.
Keywords :
phasor measurement; power system dynamic stability; time series; AD 1996 08 10; Western North American disturbance; critical slowing down; critical transition; deterministic models; dynamical behavior; fast-slow systems; model-free methods; power systems; random perturbations; statistical behavior; stochastic infinite bus model; synchronized phasor measurements; three machine-nine bus system; time series data; voltage collapse; Data models; Educational institutions; Mathematical model; Power system stability; Predictive models; Stress; Time series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
ISSN :
1944-9925
Type :
conf
DOI :
10.1109/PESMG.2013.6672928
Filename :
6672928
Link To Document :
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