A Method for Classifying Power Quality Disturbances Based on Quantum Neural Network and Evidential Fusion

A novel classifier based on integrated quantum neural networks (QNNs) and DS evidential theory to recognize the type of power quality (PQ) disturbances is presented. According to the discrete wavelet transform (DWT), wavelet packet transform (WPT) and S-transform algorithms, three kinds of feature vectors extracted from the original signals are used to train three different quantum neural networks, then DS evidential theory is used for global fusion to gain a unified classification result from the outputs of the networks. The proposed classifier has been tested on simulation signals that contain single and multiple disturbances. Simulation results indicate that the classifier has strong adaptability to the classification of power quality disturbances and achieves a high accuracy of various cases.