Power-line insulator defect detection and classification.
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Date
2018
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Abstract
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.
Description
Doctor of Philosophy (Computer Engineering). University of KwaZulu-Natal. Durban, 2017.