Electricity infrastructure is a vital foundation of modern life. Maintaining power networks with safety, precision, and efficiency remains a key challenge for utility providers.
we provide intelligent drone-based solutions for power line inspection, transmission tower assessment, and infrastructure management. Our services support automated flight planning, real-time data acquisition, and advanced analytics, helping power companies reduce risks, lower costs, and improve operational efficiency.
In this video, our LiDAR-equipped drone captures point cloud data of power lines. The elevation-based color rendering clearly distinguishes different features: the top of the power lines appears in red, mountainous terrain in green, and roads in blue. This intuitive visualization allows for efficient identification of power infrastructure and surrounding geography.
Basic measurement involves a semi-automated process in which points are selected directly on point cloud data to perform various measurements. These include conductor-related measurements such as sag, phase-to-phase distance, and the minimum distance from the conductor to the ground wire at the midpoint of a span.
It also covers clearance measurements between conductors and surrounding objects, such as vegetation, buildings, ground surfaces, and crossing lines. Additionally, it enables spatial distance measurements between any two points, with results showing the straight-line distance, horizontal distance, and vertical distance.
In this video, we again use a LiDAR-equipped drone to collect point cloud data of power lines. Unlike the previous case, this mission focuses solely on the power lines themselves. The lines are highlighted in the dataset, allowing for a clearer, more detailed view of each individual line's condition and structure.
UAVs or manned aircraft equipped with LiDAR scan power transmission lines. The collected 3D point cloud data is imported into a self-developed UAV inspection platform to build a digital record of the lines, with spatial accuracy up to the centimeter level. This supports flight path planning, 3D modeling, and condition analysis.
Point cloud analysis helps quickly identify hazards such as tree obstructions, wind deviation, and ice buildup, enabling fast reporting and supporting a shift from reactive response to proactive prevention.
Based on LiDAR point cloud data, the inspection route is planned, and the drone autonomously follows the route to perform power line inspections.
By analyzing the captured images, defects such as broken strands, loose strands, and lightning strikes can be identified.
Specialized measurement uses automated batch tasks to assess tower height, tilt, angle, GPS location, and cross-arm height differences. It also measures conductor and ground wire sag, span length, and key clearances.
Additional measurements include jumper wire sag and its nearest distance to the tower, as well as phase spacing between conductors and ground wires. Results are compared to reference standards, with anomalies automatically detected and labeled.
Conduct 3D LiDAR scanning of power lines using UAV-mounted laser systems
Process and model the collected point cloud data with high precision
Plan refined inspection routes based on the 3D model
Execute automated UAV inspection missions along the planned routes
Collect and upload aerial imagery and sensor data
Use an AI-powered image recognition platform to detect defects and risks
Manage defects through a centralized risk management platform for archiving, analysis, and corrective tracking
The collected data is processed, analyzed, and compiled into a comprehensive report, which is then sent to the client.
Clearance simulation is performed on classified point cloud data by setting safety distances for the ground, buildings, vegetation, and crossing objects. Simulation tasks are created in batches by span to identify potential risk points within the transmission corridor.
These risk points can be located, visualized on a 3D map, and exported, providing users with clear guidance to eliminate potential hazards along the corridor.
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