Power quality disturbance classification method based on fusion of multiple 1-D CNN model

Aiming at the problem of power quality disturbance (PQD) classification, classification sub-models based on one-dimension CNN in the time domain (original signal), frequency domain (Fourier transform) and time-frequency domain (wavelet transform) are modified. And then, a fusion model of classification result based on back propagation (BP) neural network is constructed to realize PQD classification. By simulation experiment and comparative analysis, the fusion method is proved to have high accuracy and good robustness under the premise of low computational complexity. The classification accuracy is higher than 99.7% with SNR not less than 30 dB, while the accuracy of 99.58%, 99.33% and 98.91% can still be maintained at 20 dB, 15 dB and 10 dB SNR. The method is proved valuable in practical application, and provides a reference for the further study of fusion method-based PQD classification.