Power quality disturbances classification using autoencoder and radial basis function neural network

Abstract

Abstract The classification of power quality (PQ) disturbances is a critical task for both utilities and industry. PQ issues cause power system equipment to fail. PQ disruptions also cause significant disruption in the paper and semiconductor industries, with significant financial implications as well as technological difficulties. Deep learning based approaches are used for automatic PQ disturbance classification, which requires huge amounts of data. A PQ disturbance dataset consisting of 12 PQ disturbances is developed using wavelet transform and MATLAB software. In this paper, an autoencoder is used to reduce the dimensionality of power quality disturbances data from higher dimensionality space, which consists of 72 input features, to lower dimensionality space, which consists of 21 input features. Based on data extracted from the autoencoder, a radial basis function neural network is used to identify the type of PQ disturbances. Based on the simulation results, it is observed that radial basis function neural network is able to distinguish the type of PQ disturbance with 92 % accuracy.