  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.
