Published Paper Details:

In recent years Power Quality has become an important issue for both utilities and customers. The increasing use of power quality sensitive equipment forced the distribution utilities to adopt a new method for continuously monitors the power quality of grid. Poor power quality may cause overheating of lines, inaccurate metering, and reduced efficiency of appliances. This dissertation proposes a new method to classify certain power quality disturbances. The power quality disturbances such as voltage sags, voltage swells, and voltage interruptions will be considered under study. In this dissertation work simulation of an electrical power system with power quality disturbances is done in MATLAB Simulink. Capturing of voltage signal is done for analyzing the power quality disturbances. The simulation results are further analyzed for the classification of power quality disturbances using MATLAB programming. The captured voltage signals are decomposed by using the signal processing techniques namely Wavelet transform. The estimation of statistical parameters is done from the decomposed signals for feature extraction. The extracted features are further used to classify the disturbances using soft computing techniques namely KNN. The proposed Wavelet-KNN approach classify these frequently occurring power quality disturbances accurately and with less computational complexity