UAV Flight Fuzzy Controller with Deep Learning Network Fault Checker of High-Voltage Lines
Keywords:
Overhead Power Line, DNN, AI, UAV, fuzzy controller, Tracking CheckerAbstract
In recent years, more and more power transmission lines have been inspected using unmanned autonomous vehicle UAVs. Intelligent UAV control using deep learning methods and machine learning has received a lot of attention due to its ability to increase inspection accuracy. This paper presents the development of a tele-powered fuzzy-controller vehicle sonar tracking checker developed by using UAV in data power line detection for high-voltage power line preventive maintenance. The Vehicle Sonar Tracking Checker is designed with portability in mind, so cable spacers, suspension clamps, and other obstacles that previously prevented inspection of high-voltage power lines do not impede inspection of the lines. In contrast, deep neural network DNN has improved the accuracy of many machines learning tasks because it can categorize and detect various errors. Deep learning on aerial photographs has been used for several applications. Drones can provide a low-cost aerial imaging platform in such applications. There is no limit to the amount of data that can be collected using drone photos to find network infrastructure. A significant amount of data can be analyzed by combining it with other technologies such as artificial intelligence (AI). This reduces the time it takes to identify and fix issues, making it easier for the team to get started and fix. The combined DNN and drone technologies can enable more effective power line maintenance, reaching areas with higher effectiveness. This reduces the time it takes to identify and fix problems, making it easier for individuals to get in and fix things..
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