Fine-Grained Distribution Grid Mapping Using Street View Imagery
Fine-Grained Distribution Grid Mapping Using Street View Imagery
复制标题
使用街景图像进行细粒度分布网格映射
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
R. Rajagopal
中科院分区：
文献类型：
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作者：
Q. Tang;Zhecheng Wang;Arun Majumdar;R. Rajagopal
细粒度的配电网测绘对于电力系统在可再生能源并网、植被管理和风险评估方面的运行和规划至关重要。然而，目前此类信息可能不准确、过时或不完整。现有的网格拓扑重建方法严重依赖于各种假设和测量数据，而这些数据并不广泛可用。为了弥补这一差距，我们提出了一种基于机器学习的方法，该方法使用易于获得的向上视角的街道视图来自动检测、定位和估计配电线路和电线杆的互连。我们在现实世界的配电网测试案例中展示了我们的方法卓越的图像级和区域级精度。
Fine-grained distribution grid mapping is essential for power system operation and planning in the aspects of renewable energy integration, vegetation management, and risk assessment. However, currently such information can be inaccurate, outdated, or incomplete. Existing grid topology reconstruction methods heavily rely on various assumptions and measurement data that is not widely available. To bridge this gap, we propose a machine-learning-based method that automatically detects, localizes, and estimates the interconnection of distribution power lines and utility poles using readily-available street views in the upward perspective. We demonstrate the superior image-level and region-level accuracy of our method on a real-world distribution grid test case.