LiDAR is currently one of the most utilized sensors to effectively monitor the status of power lines and facilitate the inspection of remote power distribution networks and related infrastructures. To ensure the safe operation of the smart grid, various remote data acquisition strategies, such as Airborne Laser Scanning (ALS), Mobile Laser Scanning (MLS), and Terrestrial Laser Scanning (TSL) have been leveraged to allow continuous monitoring of regional power networks, which are typically surrounded by dense vegetation. In this article, an unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage, as well as the surrounding vegetation in a Power Line Corridor (PLC) solely from LiDAR data. Initially, the proposed approach eliminates the ground points from higher elevation points based on statistical analysis that applies density criteria and histogram thresholding. After denoising and transforming of the remaining candidate points by applying Principle Component Analysis (PCA) and Kd-tree, power line segmentation is achieved by utilizing a two-stage DBSCAN clustering to identify each power line individually. Finally, all high elevation points in the PLC are identified based on their distance to the newly segmented power lines. Conducted experiments illustrate that the proposed framework is an agnostic method that can efficiently detect the power lines and perform PLC-based hazard analysis.

翻译：激光雷达（LiDAR）是目前最常用于有效监测电力线状态、促进远程配电网络及相关基础设施巡检的传感器之一。为确保智能电网的安全运行，已采用多种远程数据采集策略，例如机载激光扫描（ALS）、移动激光扫描（MLS）和地面激光扫描（TLS），以实现对通常被茂密植被包围的区域电力网络的连续监测。本文提出一种无监督机器学习（ML）框架，仅基于LiDAR数据来检测、提取并分析高压与低压电力线特性及电力线走廊（PLC）内的周围植被。首先，该方法根据应用密度准则和直方图阈值处理的统计分析，从较高高程点中去除地面点。通过应用主成分分析（PCA）和Kd-树对剩余候选点进行去噪与变换后，利用两阶段DBSCAN聚类实现电力线分割，以分别识别每条电力线。最后，基于所有高电高点与新分割电力线的距离进行标记。实验表明，所提框架是一种可高效检测电力线并执行基于PLC的危险分析的通用方法。