Power quality disturbances classification by ensemble and hybrid Neural networks

摘要

A novel ensemble neural network structure is presented for automatic classification of power quality disturbances. Power quality (PQ) disturbances analysis is the focus of power quality control. The characteristics of PQ disturbances include short duration, variety of types and so on. Power quality disturbances classification is the foundation of power quality control automation. Different types of Neural network, such as BP neural networks, RBF neural networks and probabilistic neural network etc, is already applied in the area of PQ disturbances classification and recognition. The researches about the neural network for PQ disturbances recognition are mainly focused on the optimizing for the signal type of neural network. But the accuracy rate of the classification is still needed to be improved. Ensemble and hybrid algorithms research is currently flourishing in pattern classification machine learning and decision sciences. Compare to traditional NN, the ensemble and hybrid NN classifier achieves higher classification rate. In this paper, a novel PQ classification system using S-transform and ensemble and hybrid NN is designed. There are 2 stages in the novel system. Firstly, the PQ disturbances signals are transformed by S-transform and the subset of features extracted from the result of S-transform is used as the input vector of the ensemble and hybrid NN. Secondly in the pattern classification process, BP network and RBF neural network are utilized as two classification agents. Through choosing different parameters and different samples, every agent includes a group of neural networks. The classification results, generated by different agents, are fuzzified into fuzzy numbers. The centroid of all fuzzy numbers is compared with the threshold. Finally we obtain the classification results. In the simulation, 6 types of disturbances signals which are simulated by Matlab 7.0 use for test the new classification system. Simulation result shows that when the new syste--m has better classification rate especially at the high noising environment.