A system for housing a drone for inspecting a section of electrical grid includes a nest connected to a power supply and positioned on a powerline structure. The nest can be configured to house a drone; charge the drone while the drone is housed; and receive an alert from a server, the alert indicating a geolocation of a detected radio frequency (RF) event. The system can also include a drone. The drone can include at least one RF sensor and can be configured to, in response to the alert being received by the nest, autonomously travel to the geolocation; perform RF measurements via the at least one RF sensor at the geolocation; upload the RF measurements to the server; and return to the nest.


    Access

    Download


    Export, share and cite



    Title :

    DRONE SYSTEM FOR POWERLINE INSPECTION USING RADIO FREQUENCY SCANNING TECHNIQUES


    Contributors:

    Publication date :

    2024-01-18


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    H02G INSTALLATION OF ELECTRIC CABLES OR LINES, OR OF COMBINED OPTICAL AND ELECTRIC CABLES OR LINES , Verlegen oder Installieren elektrischer Kabel oder Leitungen, Verlegen oder Installieren kombinierter optischer und elektrischer Kabel oder Leitungen / B64U / G01R Messen elektrischer Größen , MEASURING ELECTRIC VARIABLES



    Drone system for powerline inspection using radio frequency scanning techniques

    WONG KHOI LOON | European Patent Office | 2025

    Free access

    DRONE SYSTEM FOR POWERLINE INSPECTION USING RADIO FREQUENCY SCANNING TECHNIQUES

    WONG KHOI LOON | European Patent Office | 2024

    Free access

    DRONE SYSTEM FOR POWERLINE INSPECTION USING RADIO FREQUENCY SCANNING TECHNIQUES

    WONG KHOI LOON | European Patent Office | 2022

    Free access

    DRONE SYSTEM FOR POWERLINE INSPECTION USING RADIO FREQUENCY SCANNING TECHNIQUES

    WONG KHOI LOON | European Patent Office | 2022

    Free access

    Autonomous Drone-Based Powerline Insulator Inspection via Deep Learning

    Muhammad, Anas / Shahpurwala, Adnan / Mukhopadhyay, Shayok et al. | Springer Verlag | 2019