RESUMO
High-voltage power line insulators are crucial for safe and efficient electricity transmission. However, real-world image limitations, particularly regarding dirty insulator strings, delay the development of robust algorithms for insulator inspection. This dataset addresses this challenge by creating a novel synthetic high-voltage power line insulator image database. The database was created using computer-aided design softwares and a game development engine. Publicly available CAD models of high-voltage towers with the most common insulator types (polymer, glass, and porcelain) were imported into the game engine. This virtual environment allowed for the generation of a diverse dataset by manipulating virtual cameras, simulating various lighting conditions, and incorporating different backgrounds such as mountains, forests, plantation, rivers, city and deserts. The database comprises two main sets: The Image Segmentation Set, which includes 47,286 images categorized by insulator material (ceramic, polymeric, and glass) and landscape type (mountains, forests, plantation, rivers, city and deserts). Moreover, the Image Classification Set that contains 14,424 images simulating common insulator string contaminants: salt, soot, bird excrement, and clean insulators. Each contaminant category has 3,606 images divided into 1,202 images per insulator type. This synthetic database offers a valuable resource for training and evaluating machine learning algorithms for high-voltage power line insulator inspection, ultimately contributing to enhanced power grid maintenance and reliability.
RESUMO
The inspection and maintenance of transmission systems are necessary for their proper functioning. In this way, among the line's critical points are the insulator chains, which are responsible for providing insulation between conductors and structures. The accumulation of pollutants on the insulator surface can cause failures in the power system, leading to power supply interruptions. Currently, the cleaning of insulator chains is performed manually by operators who climb towers and use cloths, high-pressure washers, or even helicopters. The use of robots and drones is also under study, presenting challenges to be overcome. This paper presents the development of a drone-robot for cleaning insulator chains. The drone-robot was designed to identify insulators by camera and perform cleaning through a robotic module. This module is attached to the drone and carries a battery-powered portable washer, a reservoir for demineralized water, a depth camera, and an electronic control system. This paper includes a literature review on the state of the art related to strategies used for cleaning insulator chains. Based on this review, the justification for the construction of the proposed system is presented. The methodology used in the development of the drone-robot is then described. The system was validated in a controlled environment and in field experimental tests, with the ensuing discussions and conclusions formulated, along with suggestions for future work.