Aiming at the problem of defective point cloud of power equipment components due to limited scanning range of LiDAR and mutual occlusion of power equipment components in power scenario, a power equipment component LiDAR point cloud completion network Power Point Cloud Complete Net (PPC-Net) based on point feature transform is proposed in this paper. A multi-scale feature fusion encoder is used to extract global and local features of defective point clouds at different scales to avoid the problem of losing detailed features of power equipment components caused by multi-dimensional mapping, and EdgeConv is used to enhance the extraction of neighborhood information from point clouds. Then, the DT module is proposed to integrate feature transfer from parent to child points during the generation stage of fine and complete point clouds in order to preserve the local features of the generated point cloud. Next, a smooth optimization module is designed to output a complete point cloud of power equipment components with uniform distribution and smooth surface through three-level smooth sampling algorithm. Experiments on the self-built power equipment component point cloud dataset ELE and the public dataset PCN show that PPC-Net has a good completion effect on defective power equipment component point clouds and good generalization on the general shape point clouds.