Fast-training feedforward neural network for multi-scale power quality monitoring in power systems with distributed generation sources

In this paper, a deep learning approach for power quality monitoring in systems with distributed generation sources is presented. The proposed method focuses in the multi-scale analysis of mull-component signals for power quality disturbances classification. The proposed methodology combines a signal processing stage using variational mode decomposition (VMD) to obtain the times scales of mull-component signals, and a deep learning stage using a simple feedforward neural network (FFNN) to classify the disturbances. The simple proposed architecture allows minimum training time of the classification model. In addition, the proposed method is able to classify different disturbance combinations based on a reduced training-set. The proposed VMD-FFNN method is tested using synthetic and simulated signals, and it is compared with other well-known methods based on convolutional and recurrent deep neuronal networks. Finally, the proposed method is assessed using lab measurements in order to shown its performance in a real-world environment.

Automated summary

This paper presents a deep learning approach for power quality monitoring in systems with distributed generation sources, focusing on multi-scale analysis of multi-component signals for disturbance classification. The method combines VMD signal processing stage and FFNN deep learning stage, allowing minimum training time for disturbance classification. The proposed method is validated in a real-world environment through lab measurements.