Support Vector Classifier Using Basin-Based Sampling for Security Assessment of Nonlinear Power and Control Systems

Daewon Lee, Jaewook Lee

Abstract: A novel active learning method for security assessment of nonlinear systems is presented. The proposed method first extracts a dataset near the stability region boundary by using the direct method, and then learns a SVM model from the data. The constructed SVM classifier is shown to dramatically reduce the conservativness of the estimated stability region and also to make a fast security assessment.