Multilabel learning for the online transient stability assessment of electric power systems

Abstract

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