Power quality disturbance recognition is an extremely important part of power quality data analysis problems.The currently implemented power quality disturbance recognition methods generally suffer from slow recognition speed and low recognition accuracy.This paper proposes a method that is simple to calculate and can effectively recognize classification, that is, a power quality disturbance recognition method based on unidirectional representation dictionary learning.First, train the training samples of the power quality data to obtain sub-dictionaries corresponding to each type, propose a unidirectional constraint so that the direction of the coefficients of the samples in the dictionary can be distinguished.Then distinguish the type by calculating the direction and size of the representation coefficient of the test sample.The experimental results show that the method proposed in this paper not only has higher recognition accuracy than existing recognition methods, but also improves the calculation efficiency.
