Feature selection for dynamic stability prediction of power system using neural network - Nguyen, Ngọc Âu; Quyen, Huy Ánh; Phan, Viết Thịnh (Journal of Technical Education Science, 2015)
Dynamic stability prediction of power system faces with a large number of features, but not all features are useful. The redundant features will cause noise and reduce the perfor - mances of classifier. Feature selection aims to select a feature subset for classifier and improve recognition accuracy. This paper suggests the application of Relief algorithm for the feature selection. It is compared with two methods of feature selection that are Fisher discrimination and Divergence. Two recommended models for recognition accuracy are GRNN (Generalized Regression Neural Network) and MLPNN (Multilayer Perceptron Neural Network). Testing results on IEEE 39 - bus diagram show that the Relief algorithm with GRNN has yielded the results with smaller number of features and higher accuracy prediction than the other. Relief algorithm has significantly reduced the number of features while the recognition accuracy has been improved over all features.