A deep adaptive learning model for online fault diagnosis of power distribution networks

Fault diagnosis of power distribution networks is a significant task for power system operation and maintenance, which can support rapid and accurate identification, localisation and repair of various faults that may occur in power distribution networks. To improve the accuracy and efficiency of fault diagnosis, this paper proposes a deep adaptive learning model for dealing with the data-driven identification problems in power systems. In the deep adaptive learning model, an adaptive shortcut learning scheme is designed to adaptively adjust the aggregation between the convolutional layers and the attention modules. In this manner, the hidden features can be effectively captured by adaptive learning with the proposed scheme. Therefore, the proposed model can effectively make online decisions on the fault types in high-voltage AC and DC test transmission. The experimental case study and comparison study in this paper demonstrate the reliable performance of the proposed model for online fault diagnosis of power distribution networks, which also show its capability and feasibility for online implementation.