| Autor: |
Damodaran, Satheeswari, Shanmugam, Leninisha, Swaroopan, N. M. Jothi |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2917 Issue 1, p1-10, 10p |
| Abstrakt: |
The distribution and transmission of high-voltage transmission networks (HVTLNs) have become increasingly important for renewable energy. It is impossible for a power transmission network to function without an insulator. By utilizing automation and computer vision techniques, power system infrastructures such as component inspections, insulation inspections, transformer bushing inspections, etc. are monitored and controlled. The use of computer vision and component classification techniques has become increasingly popular in recent years rather than traditional methods of inspection. Using Unmanned Aerial Vehicles (UAV) images, this paper presents a system for classifying faulty insulators. In this research work noise reduction begins with pre-processing through median filters. Then, Segmenting the data is then accomplished using a clustering algorithm with k-means. From the segmented data, features are extracted using GLCM. Finally, Support Vector Machines (SVMs) are used to locate and classify, the insulator fault with higher recall and precision rate. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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