Enhancing Power System Protection with Machine Learning: A Smart and Reliable Approach for Insulator Inspection

Increasing demand for electricity due to technological advancement in every aspect of human life has increased the importance of the smooth operation of distribution lines. Distribution lines pass through mountains, deserts, forests and many other inaccessible zones. Therefore, the detection of present and future hazards to transmission lines due to the presence of anything on the line, human towns adjacent to the distribution lines, trees of forests, new plants nearer to the lines, and birds nest is an important concern. Conventionally, these lines were inspected by line men which consumes too much time, increases inspection cost because of the huge workforce, increases human life and safety risks, compromises the reliability of the data and take too much delay in the desired information. In order to cope with the aforementioned problems, an unmanned autonomous vehicle (UAV) is proposed in this study with an instant camera is proposed for distribution lines insulator inspection. This system can monitor the physical conditions of the insulator. This UAV-based system is capable to replace humans which reduces workforce costs and increases human life security and data reliability. In this study, a technician or any other expert maintains a distance and operates UAV to observe the lines. Further Machine Learning-Based algorithms help in fault classifications. It's an easy, safe, and long-lasting way to analyze transmission and distribution lines. In the local market of Pakistan, the proposed system of inspection and fault detection through imaging of distribution lines can be used by WAPDA and all DISCOs (distribution companies). It can inspect distribution lines that are unreachable where manual labor cannot inspect.