Pattern recognition techniques applied to electric power signal processing

We propose in this paper an approach whose main objective is to detect disturbances that affect an electric power signal. The method allows us to locate the time occurrence of these disturbances. The signal processing consists of determining two distributions that are based on the energy of the wavelet signal decomposition: the deviation distribution and the deformation distribution. Theses distributions are a signature of the disturbance and are able to provide an identification of the type of the problem. The method has been developed using the analysis by the Discrete Wavelet Transform (DWT). The electrical signal is decomposed into several levels by DWT. The different waveforms resolution levels allows us to detect any deviations from the sane signal. The energy distributions data obtained in the first step will be used as feature vectors for training an artificial neural network (ANN) with multilayer perceptrons (MLPs) and support vector machines (SVMs) to classify the Power Quality Disturbance (PQD).