Deep Learning based Power Distribution Network Switch Action Identification Leveraging Dynamic Features of Distributed Energy Resources

This study proposes a data-driven approach for identifying switch actions in power distribution networks. Simulated micro-phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi-phase multi-switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random-forest-based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123-node feeder modelled in GridLAB-D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.