Recognition Method for Multi-scale Sparse Power Quality Disturbance

In the traditional power quality disturbance recognition, there is a large amount of data and disturbance characteristics are dependent on subjective selection. To deal with these problems, a recognition method for multi-scale sparse power quality disturbance is proposed. Firstly, a multi-scale sparse model for power quality signal is constructed. Through the stationary wavelet transform (SWT) for the disturbance signal, its low and high frequency information is obtained. Then by compressed sampling for the disturbance signal, the dimension reduction data are obtained. Further, sparse coefficients calculated by orthogonal matching pursuit (OMP) algorithm constitute a sparse vector, which is directly inputted into the deep belief network to achieve intelligent disturbance classification. Meanwhile, to improve the recognition rate, cross-entropy algorithm is applied to find the optimal parameters such as the number of hidden layers and learning rate. Finally, in order to verify the effectiveness of the proposed method, a large number of simulation tests were performed for several typical single disturbances and mixed disturbances. The simulation results demonstrate that in the ideal environment the averaged recognition rate of this method for seven typical single disturbances and thirteen mixed disturbances is 99.0% and 97.69% respectively, and in noisy environment at least 96.71% and 94.62% respectively, which shows that the proposed method has a desirable performance in disturbance identification.