Substation Modelling and Network Security Monitoring Based on Scene Replication Technology
In view of the intelligent demand of the SGCC for safety operation of power grid equipment, a real scene reproduction technology is proposed. A novel substation safety monitoring platform is constructed using deep network architecture displayed with global and local point cloud features. The interne...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
State Grid Energy Research Institute
2021-11-01
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| Series: | Zhongguo dianli |
| Subjects: | |
| Online Access: | https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202004053 |
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| Summary: | In view of the intelligent demand of the SGCC for safety operation of power grid equipment, a real scene reproduction technology is proposed. A novel substation safety monitoring platform is constructed using deep network architecture displayed with global and local point cloud features. The internet of things (IOT) network directly uses the original point cloud as the input data of the network without the voxel or rendering operation. For power equipment objects, the local geometric dominant subspace of point cloud data is extracted to realize fast data processing, improve the data quality and estimate the point cloud normal vector. The repetition structures in the IOT safety scenario are automatically recognized to realize data augmentation and structure modelling. The proposed algorithm can minimize the fitting error between the model and the original point cloud, so that the three-dimensional model of the power equipment objects can be obtained quickly, and the intelligent understanding and high quality reconstruction of the large scene of transmission and transformation can be realized finally. On this basis, a substation IOT security monitoring platform is established, which can realize accurate modeling and simulation, visual analysis, intelligent management of substations, effectively enhancing the IOT safety monitoring capability of substations. |
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| ISSN: | 1004-9649 |