In order to provide the secure power supply, the appropriate maintenance of the power lines
and regular detection of its components becomes an essential part of most studies. However, the
traditional way of inspection and check-up are carried manually that requires labor intensity and
a high cost of equipment. Therefore the novel technology appeared to use Unmanned Aerial
Vehicles (UAV) to capture the power lines corridors along with its components, namely outdoor
insulators followed by using image processing technology to analyze and identify the insulator
surface. Furthermore, an algorithm based on machine learning is applied to detect deteriorations
of the surface. This study investigates the affecting on the insulator surface external factors such
as environmental, mechanical and others that may lead to failures such as leakage current and
flashover. The investigations and data analysis showed that there are various types of software
implementations used to detect diverse defects of the surface. However, there is no explicitly
mentioned correlation between detection technologies with the specific focus on special
deterioration type. Further research will be focused to develop the effective detection approach
for determining each damage type or pollution.
I studied energy supply in agriculture with a focus on renewable
energy at the Kazakh Agrotechnical University in Astana. Then I
completed an internship at a company that manufactures solar panels.
Currently receiving a master’s degree from the Nazarbayev University
in the department of Electrical and Computer Engineering.
Phone: +7(747) 538- 25-89