A Novel Approach for Hybrid Automatic Fault Detection and Location System for Transmission Lines

Authors

  • Sami Jan M.Tech. Scholar, Department of Electrical Engineering, Desh Bhagat University, Punjab, India https://orcid.org/0000-0002-6519-8818
  • Kuljinder Singh Khaira Assistant Professor, Department of Electrical Engineering, Desh Bhagat University, Punjab, India

Keywords:

FT, FDI, GSM, GPS, IED, Lab-DER, PIC, ST, WT

Abstract

A fault in transmission lines is a general problem rising due to a number of factors like wind, heavy snowfall, and rainfall etc. The paper presents a smart error detection and location system that is developed to effectively and perfectly specify and establish the precise spot where the fault has occurred. The system is developed using the Microcontroller programming concept and ensure an intelligent fault detection system to shorter to answer time for technological staff to identify and fix these faults. The advantage of the system is that it repeatedly detects faults, analysis, and categorizes these faults and then calculates the accurate error space from the grid station.

How to cite this article: Jan S, Khaira KS. A Novel Approach for Hybrid Automatic Fault Detection and Location System for Transmission Lines. J Engr Desg Anal 2020; 3(1): 1-6.

References

Kennedy O, Elizabeth A, Robert O et al. Monitoring and Fault Detection System For Power Transmission Using Gsm Technology.CSREA Press, 2017

Suresh S, Nagarajan R, Sakthivel L et al. Transmission Line Fault Monitoring and Identification System by Using Internet of Things. International Journal of Advanced Engineering Research and Science (IJAERS) 2007; 4(4).

Parvania M, Koutsandria G, Muthukumary V et al. Hybrid Control Network Intrusion Detection Systems for Automated Power Distribution Systems.

Jiand JA, Wang YC, Chuang CL et al. A Hybrid Framework for Fault Detection, Classification, and Location—Part I: Concept, Structure, and Methodology. IEEE Transactions on Power Delivery, August 2011

Reis GA, Chang J, Vachharajani N et al. Design and Evaluation of Hybrid Fault-Detection Systems. International Symposium on Computer Architecture (ISCA’05), 2005

Agbesi K, Okai FA. Automatic fault detection of power transmission lines using GSM technology. International Journal of Advanced Research In Science and Engineering, 2016; 5.

Chen K, Huang C, Jinliang He. Fault detection, classification and location for transmission lines and distribution systems: a review on the methods”, 1st April 2016

Jiang H, Student Member, IEEE, Jun J. Zhang, Senior Member, IEEE, Wenzhong Gao, Senior Member, IEEE, and Ziping Wu, Student Member, IEEE, “Fault Detection, Identification, and Location in Smart Grid Based on Data-Driven Computational Methods. IEEE Transactions

On Smart Grid 2014; 5(6).

Bhanuprakash M E, Arun C, Satheesh A. Automatic Power Line Fault Detector. International Journal of Advanced Research in Computer and Communication Engineering. 2017; 6(4).

Parihar VR, Jijankar S, DhoreA et al. Automatic Fault Detection in Transmission Lines using GSM Technology. International Journal of Innovative Research in Electrical, Electronics Instrumentation and Control Engineering 2018; 6(4).

Jiang JA, Member, IEEE, Cheng-Long Chuang, Member, IEEE, Yung-Chung Wang, Chih-Hung Hung, Jiing-Yi Wang, Chien-Hsing Lee, Senior Member, IEEE, and Ying-Tung Hsiao, Member, IEEE, “A Hybrid Framework for Fault Detection, Classification, and Location-Part II:

Implementation and Test Results. IEEE Transactions On Power Delivery 2011; 26(3).

Khoukhi A, Khalid MH. Hybrid computing techniques for fault detection and isolation, a review”, b New York City College of Technology, 2014.

Martins RS, Vale MRBG, Maitelli AL. Hybrid Methods For Detection And Identification of Faults In Dynamic Systems. Asian Journal of Control 2014; 17(5).

Ariza E, Correcher A, Vargas C et al. Supervision, Condition Monitoring and Fault Diagnosis System in a Hybrid Renewable Energy Systems (HRES) Laboratory. International Conference on Renewable Energies and Power Quality (ICREPQ’15) 2015; 10(13).

SunQ, LiZ, LiuZ et al. Fault Diagnosis for Smart Grid by a Hybrid Method of Rough Sets and Neural Network”, Springer-Verlag Berlin Heidelberg, 2011

Published

2020-06-25