Yiming Zhu / School of Electrical Engineering, University of Jinan, Jinan 250022, China
Yiqing Liu / School of Electrical Engineering, University of Jinan, Jinan 250022, China
Kai Wu / School of Electrical Engineering, University of Jinan, Jinan 250022, China
Model-driven distance protection cannot correctly identify the fault phases when it is applied to the DCTL (double circuit transmission lines on the same tower). This paper proposes a fault phase identification method based on CNN (convolutional neural network). The performance of the proposed method is verified by a larger number of fault data, and the results show that the proposed method can effectively identify the fault phases of the DCTL.