A data mining framework for online dynamic security assessment: Decision trees, boosting, and complexity analysis

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

12 Scopus citations

Abstract

Online dynamic security assessment provides the real-time situational awareness for assessing the impact of various N-k contingencies, so that appropriate preventive/corrective controls could be armed in a timely fashion. This task is challenging due to the large number of possible contingencies, the massive scale of power systems, and the multi-scale dynamics that occur under varying operating conditions. In this study, a data mining framework for online dynamic security assessment using decision trees and a boosting technique is developed, with the following multi-stage processing. 1) In the offline training stage, classifiers consisting of multiple simple decision trees are built based on a given collection of training data, and an iterative algorithm is used to "boost" the accuracy of the classifiers. 2) In the near real-time update stage, the simple decision trees together with their voting weights are updated when new data are available, enabling a smooth tracking of the changes of decision regions. 3) In the online DSA stage, real-time phasor measurements are used to locate the current operating condition into a decision region and obtain timely security decisions. The clustering of contingencies and data preprocessing via dimension reduction of the attributes are also discussed. Numerical testing based on a practical power system demonstrates that the proposed approach works well under a variety of realistic operating conditions.

Original languageEnglish (US)
Title of host publication2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012
DOIs
StatePublished - May 16 2012
Event2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012 - Washington, DC, United States
Duration: Jan 16 2012Jan 20 2012

Publication series

Name2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012

Other

Other2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012
CountryUnited States
CityWashington, DC
Period1/16/121/20/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture

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    He, M., Zhang, J., & Vittal, V. (2012). A data mining framework for online dynamic security assessment: Decision trees, boosting, and complexity analysis. In 2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012 [6175766] (2012 IEEE PES Innovative Smart Grid Technologies, ISGT 2012). https://doi.org/10.1109/ISGT.2012.6175766