Classification of power quality disturbances using S-transform based artificial neural networks

This paper presents a method based on S-transform and artificial neural network for detection and classification of power quality disturbances. The input features of the neural network are extracted using S-transform. The features obtained from the S-transform are distinct, understandable and immune to noise. These features after normalization are given to a feed forward neural network trained by the back propagation algorithm. The data required to develop the network are generated by simulating various faults in a test system. The proposed method requires less number of features and less memory space without losing its original property. The simulation results show that the proposed method is effective and can classify the power quality signals even under noisy environment.