{'fraction_non_alphanumeric': 0.019626168224299065, 'fraction_numerical': 0.00046728971962616824, 'mean_word_length': 5.353115727002967, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Indoor Autonomous Powerline Inspection Model

This paper presents the development of an autonomous system that leverages the Quanser Qdrone to perform above-ground indoor autonomous powerline inspections. The powerline infrastructure is exposed to various extreme weather conditions that create an operational concern for utility companies. Frequent inspections ensure the safe operation of a power transmission grid. There are mainly two methods of examination, ground and air [1]. The ground inspections are often slow and challenging due to the rough terrain, utility pole height, and inaccessible remote areas. The aerial inspections are accomplished by deploying helicopters that are expensive to operate, maintain, and repair. As an alternative, Unmanned Aerial Vehicles (UAVs) are being widely adopted for both surveillance and analysis throughout the energy and utility industries. UAVs are being used for inspections of utility towers as well as powerlines as they are energy efficient, user friendly, and convenient. Drone video capturing allows for safer, faster, and more cost-effective solutions to powerline and utility tower inspections as the user does not have to leave the ground aside from repairs. The objective of this project was to develop an autonomous UAV system to detect and track powerlines and utility poles to perform fault inspections of their electrical and material components. The proposed algorithm used a state flow machine paired with an image recognition neural network to make decisions for searching, identifying, and flying along utility poles and powerlines. The proposed system was implemented using MathWorks MATLAB and Simulink with Quarc, a third-party toolbox designed by Quanser, enabling real-time applications with the QDrone. The project yielded an algorithm that would autonomously fly the Quanser QDrone through a scan of the local area, leverage a neural network, PowerNet, to locate an initial tower within the work area, follow attached powerlines if there are any, and locate the secondary tower. Once the inspection was completed, the QDrone would return to the home point and land.