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One of the most promising approaches for complex technical systems analysisemploys ensemble methods of classification. Ensemble methods enable to build areliable decision rules for feature space classification in the presence ofmany possible states of the system. In this paper, novel techniques based ondecision trees are used for evaluation of the reliability of the regime ofelectric power systems. We proposed hybrid approach based on random forestsmodels and boosting models. Such techniques can be applied to predict theinteraction of increasing renewable power, storage devices and swiching ofsmart loads from intelligent domestic appliances, heaters and air-conditioningunits and electric vehicles with grid for enhanced decision making. Theensemble classification methods were tested on the modified 118-bus IEEE powersystem showing that proposed technique can be employed to examine whether thepower system is secured under steady-state operating conditions.

Ensemble Methods of Classification for Power Systems Security Assessment