Method for classification of power quality disturbances exploiting higher order statistics in the EMD domain

Abstract

Power quality has become a major concern recently because of increasing number of sensitive loads being connected to the power system. Degradation in power qual- ity is normally caused by power-line disturbances, malfunctions, instabilities, short lifetime, failure of electrical equipments etc. In order to improve power quality, the sources and causes of power quality (PQ) disturbance events must be known apri- ori to take appropriate mitigating actions. However, to determine the causes and sources of PQ disturbances, it is important to detect, localize and classify them. For the classi cation of PQ disturbances, a wide range of signal processing meth- ods have been reported in the literature. Since, PQ disturbance is a non-stationary signal, development of a PQ disturbances classi cation method, which is simple yet e ective in handling practical conditions, such as multiclass PQ disturbances, ran- dom selection of increased training and testing dataset, and presence of noise, is still a challenging task. In this thesis, a new method for the classi cation of PQ disturbances exploiting higher order statistics in the Empirical mode decomposi- tion (EMD) domain is proposed. A PQ disturbed signal is rst analyzed in terms of intrinsic mode functions (IMFs) by using EMD operation. The Higher Order Statistics (HOS), such as variance, skewness and kurtosis of the rst three extracted IMFs are then utilized to form the feature vector. The feature vector thus obtained when fed to the Probabilistic Neural Network (PNN) and k-Nearest Neighborhood (kNN) classi ers separately is found to be capable of classifying the multiclass PQ disturbance signals even in the presence of noise. Moreover, as expected, the clas- si cation accuracy is found to be enhanced using the proposed feature set while increasing the training and testing dataset. For the characterization of PQ dis- turbance signals, mathematical models of eleven classes of disturbances are used. Simulations are carried out to evaluate the performance of the proposed method in terms of e ciency derived from the confusion matrix and CPU time representing the computational burden. It is shown that the proposed method outperforms some of the state-of-the-art methods with superior e cacy in stringent conditions.