The Mode of Constructing Safe Trajectories of Motion of the Unmanned Aerial Vehicle while Monitoring Power Lines Considering the Influence of their Electromagnetic Fields

A.R., Shabanova; I.M., Tolstoy; I.V., Lebedev · 2019 · DOAJ

DOI: 10.5281/zenodo.3562201

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Summary

This paper addresses the challenge of enhancing flight safety and monitoring accuracy for unmanned aerial vehicles (UAVs) inspecting overhead power transmission lines. The authors identify two primary risks: mechanical collisions with line structures and electromagnetic interference (EMI) from high-voltage lines disrupting onboard electronics. Existing monitoring solutions are criticized for employing suboptimal flight trajectories, imposing non-universal flight zone constraints, and failing to account for the specific imaging requirements needed to accurately assess structural parameters like wire sag. To resolve these issues, the study proposes a comprehensive methodology for constructing safe UAV trajectories and algorithms for automated aerial imaging and wire sag determination. The methodology integrates electromagnetic field (EMF) calculations with computer vision techniques. First, the authors develop a calculation model for the distribution of electric and magnetic fields near power lines using the method of images and the Biot-Savart-Laplace equation. This model determines the minimum safe distance between the UAV and the conductors based on voltage, current, and geometric parameters, ensuring onboard equipment remains within safe field intensity thresholds. Second, the paper introduces a dual-mode aerial imaging algorithm. Unlike previous methods that rely solely on top-down views, this approach adjusts the UAV’s position relative to the wire for both top-down and side-view imaging. This adjustment prevents detection errors caused by overlapping wires in side views and ensures the conductor is centered in the camera’s field of view. For wire sag determination, the authors propose an image-processing algorithm that detects the wire’s attachment points on support structures and identifies the curve of the wire. By calculating the maximum vertical deviation from the straight line connecting the attachment points, the system determines the sag in pixels. This value is then converted to real-world meters using the camera’s focal length and known structural dimensions. The algorithm also includes a step to merge multiple images to cover the entire span between supports. Experimental validation was conducted in a simulator environment. The results demonstrated that the proposed method for determining wire sag achieved an accuracy of 90.22%. The significance of this research lies in its potential to improve the fault tolerance of power transmission networks and reduce maintenance costs. By automating the detection of structural defects and ensuring UAV safety through precise EMF-aware trajectory planning, the system enables earlier recognition of power line failures. This leads to reduced power losses and optimized service costs for overhead lines. The study provides a robust framework for UAV-based monitoring that overcomes the limitations of existing systems by integrating electromagnetic safety constraints with advanced, multi-angle imaging algorithms.

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• signal detection theory