In the presence of an MMC-HVDC system

current differential protection (CDP) has the risk of failure in operation under an internal fault. In addition

CDP may also incur security issues in the presence of current transformer (CT) saturation and outliers. In this paper

a current trajectory image-based protection algorithm is proposed for AC lines connected to MMC-HVDC stations using a convolution neural network improved by a channel attention mechanism (CA-CNN). Taking the dual differential currents as two-dimensional coordinates of the moving point

the moving-point trajectories formed by differential currents have significant differences under internal and external faults. Therefore

internal faults can be identified using image recognition based on CA-CNN. This is improved by a channel attention mechanism

data augmentation

and adaptive learning rate. In comparison with other machine learning algorithms

the feature extraction ability and accuracy of CA-CNN are greatly improved. Various fault conditions like different network structures

operation modes

fault resistances

outliers

and current transformer saturation

are fully considered to verify the superiority of the proposed protection algorithm. The results confirm that the proposed current trajectory image-based protection algorithm has strong learning and generalizability

and can identify internal faults reliably.