A Parallel RBFNN Classifier Based on S-Transform for Recognition of Power Quality Disturbances

摘要

This paper proposes a novel parallel RBFNN (Radial Basis Function Neural Network) classifier based on S-transform for recognition and classification of PQ (Power Quality) disturbances. S-transform is used to extract feature vectors, while the constructed parallel RBFNN classifier is used to recognize and classify PQ disturbances according to the extracted feature vectors. The parallel RBFNN classifier consists of eight sub-networks, each of which is only able to recognize one type of disturbance. In order to improve the convergence performance of RBFNN and optimize the number of hidden layer nodes, a dynamic clustering algorithm which clusters all training samples to determine the number of hidden layer nodes is proposed. Simulation and test results demonstrate that the method proposed to recognize and classify PQ disturbances is correct and feasible, and that the RBFNN classifier based on the dynamic clustering algorithm has a faster convergence speed and a higher correct identification rate.