Classification of voltage sag, swell and harmonics using S-transform based modular neural network

Abstract

This paper presents classification and characterization of typical voltage disturbances- sag, swell, interruption and harmonics employing S-transform analysis combined with modular neural network. S-transform is used to extract various features of disturbance signal as it has excellent time-frequency resolution characteristics and ability to detect disturbance correctly even in the presence of noise. Classification is performed using modular neural network with features extracted from S-transform. Modular neural network is designed by modifying the structure of traditional multilayer network into modules for each disturbance to provide less training period and better classification. Disturbances are characterized by magnitude and phase information using S-transform analysis. Simulation and experimental results show that S-transform combined with Modular neural network can effectively detect, classify and characterize the disturbances.