Published Paper Details:

Classification of Power Quality Disturbances (PQDs) becomes a key issue for end-users in order to enhance the Power Quality (PQ). To improve the electric power quality, sources and causes of disturbances must be known and controlled. It is necessary to classify these events in order to provide proper mitigation action as power quality disturbance waveform recognition is difficult because it involves a broad range of disturbance categories or classes. This dissertation presents S-transform (ST) based feature extraction method in which by visual inspection one can classify the power quality disturbances. S-transform can be effectively used for the analysis of power quality (PQ) disturbances and has the ability to detect the disturbance correctly. In this dissertation work three types of PQ disturbances namely voltage sag, swell and interruption are generated using the integral mathematical models of power quality disturbances implemented in MATLAB. The voltage signals obtained from integral models are further processed using ST and appropriate features are extracted. The extracted features are further used for classification of power quality disturbances using ANN.