Ensemble methods of classification for power systems security assessment

A. Zhukov, N. Tomin, V. Kurbatsky, D. Sidorov, D. Panasetsky, A. Foley

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
380 Downloads (Pure)


One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable a reliable decision rules construction for feature space classification in the presence of many possible states of the system. In this paper the novel techniques based on decision trees are used to evaluate power system reliability. In this work a hybrid approach based on random forests models and boosting model is proposed. Such techniques can be applied to predict the interaction of increasing renewable power, storage devices and intelligent switching of smart loads from intelligent domestic appliances, storage heaters and air-conditioning units and electric vehicles with grid to enhance decision making. This ensemble classification method was tested on the modified 118-bus IEEE power system to examine whether the power system is secured under steady-state operating conditions.
Original languageEnglish
Pages (from-to)45-53
Number of pages9
JournalApplied Computing and Informatics
Early online date19 Sep 2017
Publication statusPublished - Jan 2019


  • power system
  • ensemble methods
  • boosting
  • Classification
  • heuristics
  • random forests
  • security assessment

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