Application of linear discriminant analysis for classification of electrical grid emergency states

Abstract

The approach of development of relay protection algorithms based on statistical data analysis suggests a transition from traditional protections to innovative multidimensional algorithms of recognition which are able to use the available information for emergency states identification more efficiently. The state parameters used for recognition are usually combined into a single multidimensional space. This results in computational complexity of the recognition algorithm. Thus, it is advisable to implement relay protection algorithms based on generalized features. This decision will make it possible to decrease the dimension of the feature space and increase response time of the protection. The linear discriminant analysis is used to reduce the feature space dimension when recognizing the states of an electrical grid section. The training sampling for statistical analysis is formed using a simulation model of the analyzed network section. The error matrix obtained as a result of the application of k-nearest neighbors algorithm for the state classification in the developed feature space was taken as a criterion for the feature space efficiency. A method of feature space dimension reduction for the task of multidimensional relay protection was proposed. It involves generalized features of response obtained via linear discriminant analysis. The method allowed formation of a setting plane which is the most effective for different types of short-circuit classification. This setting plane provides an error-free state classification while the setting planes obtained by pairs of initial features demonstrate an error of up to 50 %. The set goal of the feature space dimension reduction was achieved. Linear discriminant analysis is advisable to use by relay protection manufacturers for reduction of the feature space dimension in multidimensional relay protection problems, including multiple classification problems.