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

Abstract

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 L1 penalty, and it inherently possesses the automatic feature reduction property desired for high-dimensional 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 L1 design. Fit for high dimensional problems, our approach demonstrates superior predictive classification accuracy.