An expert system based on S-transform and neural network for automatic classification of power quality disturbances - Uyar, Murat; Yidirim, Selcuk; Gencoglu, Muhsin Tunay (Pergamon-Elsevier Science Ltd, 2009)
In this paper, an S-transform-based neural network structure is presented for automatic classification of power quality disturbances. The S-transform (ST) technique is integrated with neural network (NN) model with multi-layer perceptron to construct the classifier. Firstly, the performance of ST is shown for detecting and localizing the disturbances by visual inspection. Then, ST technique is used to extract the significant features of distorted signal. In addition, an optimum combination of the most useful features is identified for increasing the accuracy of classification. Features extracted by using the S-transform are applied as input to NN for automatic classification of the power quality (PQ) disturbances that solves a relatively complex problem. Six single disturbances and two complex disturbances as well pure sine (normal) selected as reference are considered for the classification. Sensitivity of proposed expert system under different noise conditions is investigated. The analysis and results show that the classifier can effectively classify different PQ disturbances. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords: Power quality disturbance; S-transform; Feature extraction; Neural network; Resilient backpropagation; Classification