There are many sources of point cloud data, such as the point cloud model obtained after a bundle adjustment of aerial images, the point cloud acquired by scanning a vehicle-borne light detection and ranging (LiDAR), the point cloud acquired by terrestrial laser scanning, etc. Different sensors use different processing methods. They have their own advantages and disadvantages in terms of accuracy, range and point cloud magnitude. Point cloud fusion can combine the advantages of each point cloud to generate a point cloud with higher accuracy. Following the classic Iterative Closest Point (ICP), a virtual namesake point multi-source point cloud data fusion based on Fast Point Feature Histograms (FPFH) feature difference is proposed. For the multi-source point cloud with noise, different sampling resolution and local distortion, it can acquire better registration effect and improve the accuracy of low precision point cloud. To find the corresponding point pairs in the ICP algorithm, we use the FPFH feature difference, which can combine surrounding neighborhood information and have strong robustness to noise, to generate virtual points with the same name to obtain corresponding point pairs for registration. Specifically, through the establishment of voxels, according to the F2 distance of the FPFH of the target point cloud and the source point cloud, the convolutional neural network is used to output a virtual and more realistic and theoretical corresponding point to achieve multi-source Point cloud data registration. Compared with the ICP algorithm for finding corresponding points in existing points, this method is more reasonable and more accurate, and can accurately correct low-precision point cloud in detail. The experimental results show that the accuracy of our method and the best algorithm is equivalent under the clean point cloud and point cloud of different resolutions. In the case of noise and distortion in the point cloud, our method is better than other algorithms. For low-precision point cloud, it can better match the target point cloud in detail, with better stability and robustness.

SSC (security and stability control) system is an important facility to ensure the security and stability operation of the power grid and improve the transmission capacity. However, the differentiation of power grid characteristics and the resulting diversity of stability control strategies determine that it is difficult to standardize the stability control system, and the stability control strategy is superimposed. In the process of machine realization, the degree of automation is not high, and personalized programming leads to hidden faults that are difficult to find through conventional means. Therefore, this paper proposes an automatic realization method of SSC code based on activity diagram. First, it sorts out the basic element configuration of SSC strategy and analyzes the relationship between them. Then summarized four procedures of SSC strategy implementation, using a unified description method of the SSC strategy to associate SSC model with the strategy code of SSC device, using standardized model analysis and code Generate tools to automatically generate device codes for SSC strategies. Finally, an engineering example is given, which uses automatic code generation tools to analyze and verify the correctness of the model and the practicability of the code.

The power grid security and stability control device, as cross-bay equipment, receives and processes the sampling quantity of multiple intervals at the same time in Smart Substation. In order to solve the problem of crossinterval sampling synchronization in the “Direct Sampling” mode of stability control device, a method of combining synchronization and resampling interpolation based on local time stamp based is proposed. The sampling data based on the local time stamp which supported by system auxiliary clock, resampled synchronously and linearly interpolated at resampling interval, and analyzed the linear interpolation error. Finally, Implement the digital sampling of the stability control device based on FPGA and DSP hardware platform. In order to verify the time effectiveness and feasibility of the synchronous resampling interpolation algorithm based on local time stamp proposed in this paper from the aspects of sampling accuracy and synchronization effectiveness, an experimental system is built. The results of experiment prove that the synchronous linear resampling method based on local time stamp meets the real-time and precision requirements of the stability control device in practical engineering.