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Under Ground Cable Fault Detection Using Machine Learning Algorithm

Authors: P.I.D.T. Baladuraikannan; D. Pandiaraj; P. Anitha; S. Anitha;

Under Ground Cable Fault Detection Using Machine Learning Algorithm

Abstract

In this project work is to detect the location of fault in underground cable lines from the base station in km using an ARDUINO controller and also a Machine Learning Algorithm. The concept uses in this paper is Ohm’s law which states that current flow through the cable depends on the length of fault occur in the cable. The prototype is modeled with a set of load sensors representing cable length in km and fault creation is made by a load sensor at every known distance to cross check the accuracy of the same. In case of fault, the voltage across load sensor changes accordingly, which is then fed to a programmed ARDUINO that further displays fault location in distance. The fault occurring distance, phase, and time is displayed on LCD. IOT is used to display the location information over Internet using GSM MODULE.

{"references": ["1.\tKalia R.R., Abrol P. Design and implementation of wireless live wire fault detector and protection in remote areas. EEE. 2014, 97(17)", "2.\tClegg B. Underground Cable Fault Location. New York: McGraw- Hill. 1993.", "3.\tChoi M.S., Lee D.S., Yang X. A line to ground fault location algorithm for underground cable system. KIEE Trans. Power Eng. Jun. 2005, 267\u2013273p.", "4.\tBascom E.C. Computerized underground cable fault location expertise. In Proc. IEEE Power Eng. Soc. General Meeting, Apr. 10\u201315, 1994, 376\u2013382p.", "5.\tMaxwell L.C. A Treatise on Electricity and Magnetism, Oxford: Clarendon, 1892, 3(2), 68\u201373p."]}

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Keywords

Machine learning algorithm, ARDUINO controller, load sensor, IOT, http://www.hbrppublication.com/

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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