Fault Identification in Power Network Based on Deep Reinforcement Learning

With the integration of alternative energy and renewables, the issue of stability and resilience of the power network has received considerable attention. The basic necessity for fault diagnosis and isolation is fault identification and location. The conventional intelligent fault identification method needs supervision, manual labelling of characteristics, and requires large amounts of labelled data. To enhance the ability of intelligent methods and get rid of the dependence on a large amount of labelled data, a novel fault identification method based on deep reinforcement learning (DRL), which has not received enough attention in the field of fault identification, is investigated in this paper. The proposed method uses different faults as parameters of the model to expand the scope of fault identification. In addition, the DRL algorithm can intelligently modify the fault parameters according to the observations obtained from the power network environment, rather than requiring manual and mechanical tuning of parameters. The methodology was tested on the IEEE 14 bus for several scenarios and the performance of the proposed method was compared with that of population-based optimization methods and supervised learning methods. The obtained results have confirmed the feasibility and effectiveness of the proposed method.