Applications of signal processing techniques and artificial neural networks for power quality disturbance analysis

Abstract

The evaluation of electrical power systems during recent periods increases the interest in Power Quality (PQ). The increasing utilization of non-linear and sensitive loads, for instance the power supplies in computers, communication and consumer electronics leads to gradual deterioration of the Power Quality. This work is focused on the development of signal processing techniques and Artificial Neural Networks (ANN) for the automatic detection and classification of disturbance waveforms. In this thesis, Wavelet Transform based energy distribution and S-Transform contour based techniques are applied to analyze and detect the disturbances in time-frequency domain. This thesis proposes Radial Basis Function (RBF) network for disturbance classification. The RBF networks take less time for training and distance-based activation function used in the hidden nodes gives the ability to detect the outliers during classification. In this work, the features of the disturbance waveforms are extracted by applying standard statistical techniques to the transformed signal and a novel entropy based feature selection technique is used to select the most useful features of the network. These features are given as input to the ANN for further classification. During the training stage, the network captures the relation between the input features and the output class. After training, the network is tested with the test data to assess the generalization ability of the network. Simulation results are included in the thesis to demonstrate the validity of the proposed methods. newline newline newline