Detection Of Fault Insulator Based On Improved Convolution Neural Network

Mohd Rahul, Mohd Rafiq (2023) Detection Of Fault Insulator Based On Improved Convolution Neural Network. [Final Year Project Report] (Unpublished)

[img] PDF
Mohd Rahul 24 pgs.pdf

Download (1MB)
[img] PDF (Please get the password by email to repository@unimas.my , or call ext: 082-583914/3973/3933)
Mohd Rahul.pdf
Restricted to Registered users only

Download (0B)

Abstract

An insulator is an essential component of a transmission line, serving to prevent the leakage of electricity flow from the conductors into the ground. It accomplishes this by creating a barrier between the conductors and the supporting structure. The insulator's atomic structure consists of electrons that are strongly bound and exhibit limited mobility. Researchers have researched a variety of methods for detecting insulators through the use of image processing in previous studies. The majority of contemporary detection systems use classifiers for this purpose. These methods use a classifier trained on a training set of images to recognise an object in a test image, despite the fact that there are a few drawbacks in terms of detection precision and speed. This thesis proposes a method for constructing a hybrid YOLOv5-Resnet50 system, with Resnet50 serving as the backbone of the YOLOv5 architecture. Using a hybrid of alternating and altering the backbone of the YOlOv5s structure, the proposed method achieves an accuracy of 99.0 ± 0.233% and a training time of 25 minutes for a set of 1,000 insulator images. This proposed method has the potential to aid in the inspection of high-up insulators, and it aims to reduce the manpower required to perform this task, which is one of the most dangerous and has a high fatality rate due to its high-voltage field and high-altitude placement. Future plans include expanding the dataset size in order to enhance the system further. Next is utilising a very high-end Specification of equipment by utilising a very excellent GPU and CPU to train the data more effectively.

Item Type: Final Year Project Report
Uncontrolled Keywords: Insulator, transmission line, serving to prevent the leakage of electricity flow
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Faculties, Institutes, Centres > Faculty of Engineering
Depositing User: Unai
Date Deposited: 17 Oct 2023 08:26
Last Modified: 13 Feb 2024 01:53
URI: http://ir.unimas.my/id/eprint/43115

Actions (For repository members only: login required)

View Item View Item