School of Electrical and Information Engineering,Changsha University of Science and Technology
Abstact: In view of the low correct rate of power quality disturbance identification under the problem of noise interference, a new method of power quality disturbance classification based on deep belief network was proposed. A smooth wavelet multiscale transform is performed on the power quality disturbance signal, and then the soft threshold function is used to process the estimated wavelet coefficients for reconstructing the original signal. Further, it is proposed to use deep confidence network to classify and recognize the reconstructed single disturbance signal and multiple disturbance signals. The simulation results demonstrate that the correct rate of this method for seven typical single disturbances and thirteen mixed disturbances is high. Even under 20dB noise interference, the classification correct rate is as high as 93% or more, which proves that the method has a strong ability to resist noise interference.