Artificial neural network for power system dynamic stability recognition - Nguyen, Ngọc Âu; Quyen, Huy Ánh; Nguyen, Văn Trọng (Journal of Technical Education Science, 2015)
Feature selection is very important data-processing technique in dynamic stability rec- ognition of power systems. This paper presents a ranking method for feature selection that bases on Fisher, Divergence, Bhattacharyya distance and Correlation coefficient. The aim is to select features with the highest data discrimination. Multilayer Feed-forward Neural Network has been applied for power system dynamic stability recognition. The proposed approach has been tested on the IEEE 30-bus power system. Many experiments have been done with the aim of finding parameters for optimal neural network performance. In comparison between Levenberg- Marquardt and Scaled Conjugate Gradient learning algorithms, results show that Levenberg-Marquardt algorithm with Fisher distance for feature selection yields less number of features and higher recognition accuracy than the others.