Sign up Log in
rjees logo



INSULATOR CLASSIFICATION USING SCALE INVARIANT FEATURE TRANSFORM WITH K-NEAREST NEIGHBOUR

Authors: *Iruansi U, Oyebode K.O

ABSTRACT

The demand of uninterrupted supply of electricity is equally increasing the need to monitor the electric power grid. Insulators are part of the components that make up the electric power grid. Hence, faulty insulators may affect the mechanical and electrical performance of an electric power grid, which can lead to the flow of leakage currents through the line supports. The traditional method of inspection is inadequate in meeting the growth and development of the present electric power grid. Hence an automated system such as the computer vision method is presently being explored as a means to resolve this crisis safely, speedily and accurately. This paper presents a method of insulator classification using scale invariant feature transform and k-nearest neighbor. The case study validates the efficacy of the proposed methodology for insulator classification.


Affiliations: *Department of Computer Engineering, Faculty of Engineering, University of Benin, PMB 1154, Benin City, Nigeria
Keywords: Active Contour, Classification, Insulator, K-nearest Neighbour, Scale Invariant Feature Transform
Published date: 2018/06/30

Download Full Text

SUBMIT A MANUSCRIPT

ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

Indexing & Abstracting
google scholar Directory of research journal indexing JIFactor Info base index scientific journal impact factor

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License



(+234) 806 927 5563

Chemical Engineering Department, Faculty of Engineering, University of Benin, PMB 1154, Ugbowo, Benin City, Edo State, Nigeria.