In order to solve the redundancy of discriminative dictionary learning (DDL) in the sparse representation of the power quality disturbance identification, the dictionary learning with structured incoherence (DLSI) is proposed to make discriminant dictionary more concise. Firstly, the various types of power quality disturbances are trained to obtain subdictionaries, public dictionary, and discriminant dictionary. Then, the sparse representation of the reduced dimension test signal is solved by the method of discriminative dictionary optimization. Finally, using sparse representation reconstruction method to solve the test samples, and the type of power quality disturbance signals are determined by the minimum of the residual error. DLSI could directly drive the discriminative dictionary to can discriminate various types of power quality disturbances, and could obtain a more compact and discriminative sparse dictionary to improve the final recognition rates for identification of power quality disturbances. The experimental results demonstrate that the average recognition rate is higher than 96% for identifying eight types of power quality disturbances, while the classification accuracy decreases slightly with the ratio of signal to noise ratio (SNR) varying from 40, 30 to 20 dB. The simulation results show that DLSI can effectively identify different types of power quality disturbance signals and improve the accuracy of the identification results, in the meantime, DLSI algorithm shows better classification and recognition performance than DDL algorithm.