Power quality disturbance classification using Hilbert transform and RBF networks

This paper presents the application of Hilbert transform and artificial neural network (ANN) for power quality (PQ) disturbance classification. The input features of the ANN are extracted from the envelope of the disturbance signals by applying Hilbert transform (HT). The features obtained from the Hilbert transform are distinct, understandable and immune to noise. These features after normalization are given to the radial basis function (RBF) neural network. The data required to develop the network are generated by simulating various faults in a test system. The performance of the proposed method is compared with the existing feature extraction techniques in combination with other ANN architectures. Simulation results show the effectiveness of the proposed method for power quality disturbance classification.