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

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2 Author(s)

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.

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Neural Networks, 2006. IJCNN '06. International Joint Conference on

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