Deep Wavelet-Based Convolutional Transformer Network in Power Quality Disturbances Classification

Real time power quality monitoring is important to ensure stable functioning of the electrical appliances especially for the manufacturing sector. Deep- WT-ConvT is proposed to to better characterise and differentiate the minor differences between different types of power quality disturbances. However, the use of deep networks requires longer training time, and poses the risk of getting internal covariant shift issues due to distribution change in layer's input during training phase. This issue can be prevented by proper parameter initialisation and with lower learning rate, which slows down the training process. Batch normalisation (BN) layers are proposed to improve the classification performance of the PQD classifier network WT-ConvT. Results shows significant improvement on Deep- WT-ConvT model with accuracy improvement from 92.95% without BN layers to 94.44% with BN layers on 20dB SNR AWGN noise test.