Voltage Sag Evaluation during Induction Motors Starting Using Artificial Neural Network

One of the most important concerns in electrical systems is to deliver energy to the consumers with high power quality (PQ). Because of great importance of voltage sag among all PQ events, this paper presents evaluation of voltage sags caused by induction motors (IMs) starting. Malfunctioning or failure of sensitive loads is main effect of this phenomenon. Both depth and duration of voltage sag are evaluated in this work using artificial neural network (ANN). Both multilayer perceptron (MLP) and radial basis function (RBF) structures have been analyzed. Six learning algorithms, backpropagation (BP), delta-bar-delta (DBD), extended delta-bar-delta (EDBD), directed random search (DRS), quick propagation (QP), and levenberg marquardt (LM) were used to train the MLP. The simulation results show that proposed technique can estimate the voltage sag characteristics with good accuracy. Also, it is shown that the LM and EDBD algorithms present better performance for evaluating of voltage sag magnitude and duration.