Comparison of ensemble decision tree methods for on-line identification of power system dynamic signature considering availability of PMU measurements

Tingyan Guo, P. Papadopoulos, P. Mohammed, J. V. Milanovic

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

4 Citations (Scopus)

Abstract

This paper compares the most commonly used ensemble decision tree methods for on-line identification of power system dynamic signature considering the availability of Phasor Measurement Units (PMU) measurements. Since previous work has shown that the surrogate split method included in classification and regression tree is not good enough to handle the unavailability of measurement signals, more effective methods are needed to be explored. Bagging, boosting and random forest methods are investigated and compared in this work. When evaluating their performance, all possible scenarios of missing PMU measurements are tested for the test network. For each ensemble decision tree model, the result is presented as a probabilistic classification error depending on the availability of PMU signals. The test network used is the 16-machine, 68-bus reduced order equivalent model of the New England Test System and the New York Power System.

Original languageEnglish
Title of host publication2015 IEEE Eindhoven PowerTech, PowerTech 2015
Place of PublicationPiscataway, NJ.
ISBN (Electronic)9781479976935
DOIs
Publication statusPublished - 31 Aug 2015
EventIEEE Eindhoven PowerTech, PowerTech 2015 - Eindhoven, Netherlands
Duration: 29 Jun 20152 Jul 2015

Conference

ConferenceIEEE Eindhoven PowerTech, PowerTech 2015
CountryNetherlands
CityEindhoven
Period29/06/152/07/15

Keywords

  • Bagging
  • boosting
  • decision tree
  • ensemble method
  • missing values
  • phasor measurement unit
  • power system dynamic signature
  • random forest

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