Li Ruiheng, Gan Lu, Liu Yang, Di Yi, Wang Chao
School of Information Engineering, Hubei University of Economics, Wuhan, 430205, China.
State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing, 400044, China.
Sci Rep. 2023 Sep 26;13(1):16092. doi: 10.1038/s41598-023-42401-w.
Using point cloud to reconstruct the 3D model of a substation is crucial for smart grid operation. Its main objective is to swiftly capture equipment point cloud data and align each device's model within the large and noisy point cloud scene of the substation. However, substation reconstruction needs improvement due to the low efficiency of traditional noise-resistant clustering methods and challenges in accurately classifying similar-looking electrical equipment. This paper proposes an automatic modeling framework for large-scale substation point cloud scenes. Firstly, we reduce the substation scene's dimensionality to improve clustering efficiency and establish relationships between data dimensions using a re-clustering algorithm. Next, a neural network is developed to identify various device types within clusters, even with limited subdivisions. Finally, a model library is employed to register standard models onto the target device's point cloud, obtaining device types and orientations. Real substation data processing demonstrates the ability to rapidly extract devices from complex and noisy point cloud scenes, effectively avoiding missegmentation issues. The automatic modeling approach achieves a precise substation calculation rate of 92.86%.
利用点云重建变电站的三维模型对于智能电网运行至关重要。其主要目标是快速获取设备点云数据，并在变电站庞大且嘈杂的点云场景中对齐每个设备的模型。然而，由于传统抗噪聚类方法效率低下以及在准确分类外观相似的电气设备方面存在挑战，变电站重建仍有待改进。本文提出了一种针对大规模变电站点云场景的自动建模框架。首先，我们降低变电站场景的维度以提高聚类效率，并使用重新聚类算法建立数据维度之间的关系。接下来，开发了一个神经网络来识别聚类中的各种设备类型，即使在细分有限的情况下。最后，使用模型库将标准模型注册到目标设备的点云上，获取设备类型和方向。实际变电站数据处理表明，该方法能够从复杂且嘈杂的点云场景中快速提取设备，有效避免误分割问题。该自动建模方法实现了92.86%的精确变电站计算率。
