Other types of classification algorithm include applying genetic based optimization technique[7], randomly optimized neural network combined with discrete wavelet transform and fuzzy logic[8] and Gabor-Wigner transform[9] for detection

This paper presents classification and characterization of typical power quality disturbancessag, 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. Wavelet analysis is also performed and classification is performed with multilayer and modular neural networks. Simulation and experimental results show that Stransform combined with Modular neural network can effectively detect, classify and characterize the disturbances.