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Turkish Journal of Electrical Engineering and Computer Sciences

Abstract

Dynamic security assessment of a large power system operating over a wide range of conditions requires an intensive computation for evaluating the system's transient stability against a large number of contingencies. In this study, we investigate the application of multilabel learning for improving training and prediction time, along with the prediction accuracy, of neural networks for online transient stability assessment of power systems. We introduce a new multilabel learning method, which uses a contingency clustering step to learn similar contingencies together in the same multilabel multilayer perceptron. Experimental results on two different power systems demonstrate improved accuracy, as well as significant reduction in both training and testing time.

DOI

10.3906/elk-1805-151

Keywords

Dynamic security assessment, transient stability assessment, multilabel learning, neural networks

First Page

2661

Last Page

2675

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