Transient stability assessment in large-scale power systems using sparse logistic classifiers

In this paper, the problem of transient stability assessment is formulated as a pattern recognition problem. The transient stability boundary (TSB) separates the region between the secure and unsecure operation conditions. In large-scale power networks, the TSB is a very high dimensional hyperplane. A modern machine learning method called the sparse logistic classifier is applied for finding the TSB. This approach combines the classical logistic classifier with a L-1 penalty, and it inherently possesses the automatic feature reduction property desired for highdimensional modeling. This methodology is demonstrated by a 470-bus power network, and compared with several competing methods recently applied in this field. These competing methods include the support vector machine (SVM) and the k-nearest neighbor (kNN) classifier, as well as the classical logistic classifier which is not equipped with the L-1 design. Fit for high dimensional problems, our approach demonstrates superior predictive classification accuracy.

Automated summary

In this paper, the problem of transient stability assessment is treated as a pattern recognition problem. The sparse logistic classifier is used to find the transient stability boundary (TSB) in large-scale power networks. The methodology demonstrates superior predictive classification accuracy in comparison to other competing methods.