Wavelet Transform for Classification of Voltage Sag Causes using Probabilistic Neural Network

This paper presents an algorithm to detect and classify voltage sag causes based on Wavelet Transform (WT) and Probabilistic Neural Network (PNN). A technique is required which is capable of extracting both time-frequency information to identify the causes which contribute to power quality disturbances. Wavelet transform based on multiresolution analysis is used to extract the features from the disturbance signal. The detailed coefficients of wavelet transform of first three levels of each disturbance are used as inputs to PNN for identification of voltage sag causes. Three voltage sag causes are taken for classification (i) Three phase short circuit (ii) Starting of induction motor and (iii) Three phase transformer energization. Simulation results show that wavelet transform combined with probabilistic neural network can effectively detect and classify the voltage sag causes.