A Hybrid Approach To Power Quality Problems In Distribution Systems

Abstract

It is expected that an electrical power system will continuously transmit nominal rated sinusoidal voltage and current to consumers. However, the widespread use of power electronics has brought with it power quality problems. In this study, classification of power quality disturbances was performed using artificial neural network (ANN). The most appropriate ANN structure was determined by the Box-Behnken experimental design method. Nine disturbance types (no fault, voltage sag, voltage, swell, flicker, harmonics, transient, DC component, electromagnetic interference, instant interruption) were investigated in computer simulations. The feature vectors used in the identification of the different types of disturbances produced were obtained with the help of discrete wavelet transform and principal component analysis. When the simulation results are analyzed, it is observed that the optimized feed forward multilayer ANN structure successfully distinguishes power quality disturbances.