Title :
Support Vector Classifier Using Basin-Based Sampling for Security Assessment of Nonlinear Power and Control Systems
Author :
Lee, Daewon ; Lee, Jaewook
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 conservativeness of the estimated stability region and also to make a fast security assessment.
Keywords :
learning (artificial intelligence); nonlinear control systems; power engineering computing; power system control; power system security; stability; support vector machines; active learning method; basin-based sampling; nonlinear control systems; nonlinear power systems; security assessment; stability region boundary; support vector classifier; Control systems; Data security; Learning systems; Nonlinear control systems; Nonlinear systems; Power system security; Sampling methods; Stability; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.247035
