Insulator Fault Detection Based on Spatial Morphological Features of Aerial Images

Because insulators provide electrical insulation and mechanical support for electric transmission lines, these components are of paramount importance to safe and reliable operations of power systems. However, insulators are often considered to be prone to different faults, e.g., bunch-drop, which demands a novel solution for accurate fault detection and fault location. Current research efforts have primarily focused on the bunch-drop fault of glass insulators, and the study of ceramic insulators has not been reported to date. To this end, this paper proposes an algorithmic solution for the bunch-drop fault detection for both glass and ceramic insulators based on spatial morphological features, which can be integrated into an unmanned aerial vehicle-based inspection system. Color models can be established based on the unique color features of both glass and ceramic insulators. Next, the target areas of the insulators can be identified according to the color determination combined with the insulator’s spatial features. The target area is morphologically processed to highlight the fault location, and the rules are established based on the spatial feature differences between the insulators with and without faults. Consequently, the fault location can be accurately identified, and the coordinates can be determined. The performance of the proposed solution is evaluated in comparison with existing solutions. The numerical results demonstrate that the proposed solution can detect the bunch-drop faults of insulators with a better than average detection rate. In addition, the performance is assessed and validated in terms of robustness and real-time performance.