Power-line insulator defect detection and Classification.
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Faulty insulators may compromise the electrical and mechanical integrity of a power delivery system, leading to leakage currents owing through line supports. This poses a risk to human safety and increases electrical losses and voltage drop in the power grid. Therefore, it is necessary to monitor and inspect insulators for damages that could be caused by degradation or any accident on the power system infrastructure. However, the traditional method of inspection is inadequate in meeting the growth and development of the present power grid, hence automated systems based on computer vision method are presently being explored as a means to solve this problem speedily, economically and accurately. This thesis proposes a method to distinguish between defectuous and nondefectuous insulators from two approaches; structural inspection to detect broken parts and a study of hydrophobicity of insulators under wet conditions. For the structural inspection of insulators, an active contour model is used to segment the insulator from the image context, and thereafter the insulator region of interest is extracted. Then, di erent feature extraction methods such as local binary pattern, scale invariant feature transform and grey-level co-occurrence matrix are used to extract features from the extracted insulator region of interest image and then fed into classi ers, such as a support vector machine and K-nearest neighbour for insulator condition classi cation. For the hydrophobicity study of the insulator, an active contour model is used to segment water droplets on the insulator, and thereafter the geometrical characteristics of the water droplets are extracted. The extracted geometrical features are then fed into a classi er to assess the insulator condition based on the hydrophobicity levels. Experiments performed in this research work show that the proposed methods outperformed some existing state-of-the-art methods.