Fast online identification of power system dynamic behaviour

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper discusses the methodology for fast prediction of power system dynamic behavior. A combination of features that can be obtained from PMU data is proposed, that can improve the prediction time while keeping high accuracy of prediction. Several combinations of features including generator rotor angles, kinetic energy, acceleration and energy margin are used to train and test decision trees for the online identification of unstable generator groups. The predictor importance for trained decision trees is also calculated to highlight in more detail the effect of using different predictors.

Bibliographical metadata

Original languageEnglish
Title of host publicationIEEE PES General Meeting
Publication statusPublished - 2017
Event2017 IEEE PES Society General Meeting: Energizing a More Secure, Resilient & Adaptable Grid - Chicago, United States
Event duration: 16 Jul 201720 Jul 2017
http://www.pes-gm.org/2017/

Conference

Conference2017 IEEE PES Society General Meeting
Abbreviated titleIEEE PES GM 2017
CountryUnited States
CityChicago
Period16/07/1720/07/17
Internet address