用视觉模型自动识别变电站设备，提升电网安全评估效率
Comparing Object Detection Models for Electrical Substation Component Mapping
- 对比YOLOv8、YOLOv11和RF-DETR在变电站图像上的检测表现
- RF-DETR在精度与效率上综合最优，适合大规模应用
- 为电网基础设施智能巡检提供可落地的机器学习方案
变电站是电力网络的关键组成部分，其设备（如变压器）易受飓风、洪水、地震及地磁感应电流（GIC）等灾害影响。由于电网属于国家级关键基础设施，任何故障都可能造成重大经济损失与公共安全风险。为预防和减轻此类风险，需准确识别关键设备以量化脆弱性。传统人工测绘耗时费力，因此采用计算机视觉模型实现自动化测绘更具优势。本文在人工标注的美国变电站图像数据集上训练并比较了YOLOv8、YOLOv11和RF-DETR三种模型的检测准确率、精确率与效率。结果表明，各模型各有优劣，其中RF-DETR在精度与速度间表现最佳，具备实现大规模变电站组件地图绘制的潜力。研究还展示了这些模型在美国变电站组件映射中的实际应用效果，验证了机器学习在电网资产管理中的可行性。
原文摘要 · Abstract (English)
Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are prone to disruption from many hazards, including hurricanes, flooding, earthquakes, and geomagnetically induced currents (GICs). As electrical grids are considered critical national infrastructure, any failure can have significant economic and public safety implications. To help prevent and mitigate these failures, it is thus essential that we identify key substation components to quantify vulnerability. Unfortunately, traditional manual mapping of substation infrastructure is time-consuming and labor-intensive. Therefore, an autonomous solution utilizing computer vision models is preferable, as it allows for greater convenience and efficiency. In this research paper, we train and compare the outputs of 3 models (YOLOv8, YOLOv11, RF-DETR) on a manually labeled dataset of US substation images. Each model is evaluated for detection accuracy, precision, and efficiency. We present the key strengths and limitations of each model, identifying which provides reliable and large-scale substation component mapping. Additionally, we utilize these models to effectively map the various substation components in the United States, showcasing a use case for machine learning in substation mapping.
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