Classification of power quality disturbances based on optimized least squares support vector machine

This paper presents a classification method of power quality disturbances based on least squares support vector machine (LS-SVM). Through this method, the mixed power quality disturbances in grid can be classified and identified in detail. Further more, for the characteristics of the power quality disturbances feature vector using improved shuffled frog leaping algorithm (SFLA) and cross validation to achieve the optimal classifier. It effectively solves the SVM model optimization problem. Simulation and engineering results show that the optimized classifiers not only output high classification accuracy in small training set case, but also improve the classification performance further and effectively avoid the state of excessive learning.