A new method of power quality disturbance classification based on deep belief network

Abstract

Abstract In view of the low accuracy of single disturbances 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. The simulation results demonstrate that the recognition rate of this method for seven typical single disturbances is high. Even under 20dB noise interference, the classification accuracy rate is as high as 93% or more, which proves that the method has a strong ability to resist noise interference.