Wavelet Based Signal Processing Technique for Classification of Power Quality Disturbances

This paper presents an effective method for classification of power quality disturbances, employing wavelet transformation for disturbance identification and Modular artificial Neural Network(MANN) technique for accurate classification of these disturbances. Disturbances such as voltage sag, swell and harmonics which are typical in power system are simulated. Wavelet transform, which has the ability to analyze these power quality problems simultaneously in both time and frequency domain is used to extract features of the disturbances by decomposing the signal using multi resolution analysis. These features are used to detect and localize the disturbances. ANN, the powerful tool with parallel processing capability, is suitable to classify the disturbances. Modular neural network is employed in this paper for automatic classification of power quality disturbances. The proposed algorithm has been verified by simulating various PQ disturbances and results are analyzed using Math works MATLAB.