Automatic Classification of Voltage Sags Based on Advanced Two-stage Feature Extraction Techniques

With the wide spread of power quality monitors in smart grid, information-oriented use of power quality disturbances has become a concerned research area. In this paper, a two-step strategy classification system for various voltage sags is developed. A typical classification system is composed of one selector of discriminative feature and one classifier which the former is fundamental and critical. Further research on classification ability of physical features with respect to a given classification task is necessary. By studying the intrinsic relations between features and classification task, an automatic feature selection based classification system is presented. The main advantages of the proposed method are its ability to select features highly related to the given classification task, and its ability to reduce the dimensionality of the feature set simultaneously. In order to validate the proposed method, three common voltage sags causes, namely grid fault, large motor starting and transformer energizing, are considered. Simulating and practical recorded data are tested. Experimental results have shown that the proposed method is accurate, time-saving, and robust.