Fault Detection in Power Distribution Systems Based on Gated Recurrent Attention Network

Abstract

To improve fault identification accuracy in power distribution systems, a model named gated recurrent attention network is proposed. First, a higher weight is put on the key cycles of fault phase based on the attention mechanism, making the model focus more on these key messages by weight assignment. Then, the gated recurrent network is adopted, which controls the memory transmission with gate signal and constructs the relationship between input waveform and probability of events at different stages to process the waveform sequence, thereby improving recognition accuracy. Experiments based on both simulation and field data show that the proposed method, under the small-sample-learning condition, is much better than other commonly-used classification models, such as support vector machine, gradient boosting decision tree, and convolutional neural network, providing new insights into fault identification technology in power distribution systems.

Keywords

• power distribution systems
• faults identification
• attention mechanism
• gated recurrent units (gru)