Line Outage Localization Using Phasor Measurement Data in Transient State
Abstract
This paper 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.
Additional Information
© 2015 IEEE. Manuscript received March 02, 2015; revised June 09, 2015; accepted July 10, 2015. Date of publication September 17, 2015; date of current version May 02, 2016. Paper no. TPWRS-00286-2015.Additional details
- Eprint ID
- 67643
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- CaltechAUTHORS:20160603-091450002
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2016-06-03Created from EPrint's datestamp field
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2021-11-11Created from EPrint's last_modified field