  Probabilistic security assessment and real-time dynamic security assessments
(DSA) are promising to better handle the risks of system operations. The
current methodologies of security assessments may require many time-domain
simulations for some stability phenomena that are unpractical in real-time.
Supervised machine learning is promising to predict DSA as their predictions
are immediately available. Classifiers are offline trained on operating
conditions and then used in real-time to identify operating conditions that are
insecure. However, the predictions of classifiers can be sometimes wrong and
hazardous if an alarm is missed for instance.
  A probabilistic output of the classifier is explored in more detail and
proposed for probabilistic security assessment. An ensemble classifier is
trained and calibrated offline by using Platt scaling to provide accurate
probability estimates of the output. Imbalances in the training database and a
cost-skewness addressing strategy are proposed for considering that missed
alarms are significantly worse than false alarms. Subsequently, risk-minimised
predictions can be made in real-time operation by applying cost-sensitive
learning. Through case studies on a real data-set of the French transmission
grid and on the IEEE 6 bus system using static security metrics, it is
showcased how the proposed approach reduces inaccurate predictions and risks.
The sensitivity on the likelihood of contingency is studied as well as on
expected outage costs. Finally, the scalability to several contingencies and
operating conditions are showcased.
