Transmission Line Fault Classification using Supervised Machine Learning
D. Ajay Sai Reddy1, Sheelam Divya2, Islavath Nikhil Bhargav3, Anjuri Bhanusree4, Chiripalli Neha5
1D. Ajay Sai Reddy, Department of Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad (Telangana), India.
2Sheelam Divya, Department of Electrical and Electronics Engineering, Anurag group of Institutions, Hyderabad (Telangana), India.
3Islavath Nikhil Bhargav, Department of Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad (Telangana), India.
4Anjuri Bhanusree, Department of Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad (Telangana), India.
5Chiripalli Neha, Department of Electrical and Electronics Engineering, Anurag Group of Institutions, Hyderabad (Telangana), India.
Manuscript received on 19 September 2022 | Revised Manuscript received on 25 September 2022 | Manuscript Accepted on 15 October 2022 | Manuscript published on 30 October 2022 | PP: 47-49 | Volume-12 Issue-1, October 2022 | Retrieval Number: 100.1/ijeat.A38321012122 | DOI: 10.35940/ijeat.A3832.1012122
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Abstract: Among Generation, Transmission and Distribution systems of power systems 80-90 % of faults occur in transmission lines. A small electricity interruption or disturbances can cause major problems in transmission lines. Major faults in transmission lines occur because of environmental events like high temperatures, animals, wind, etc and also faulty devices connected to transmission lines are also a major cause for these faults. In modern power systems line faults are one the major and significant problems leading to high power losses and electrical devices failure. So, it is necessary to recover these problems in transmission lines immediately to avoid major problems and also need to eliminate the faults quickly. There are few tools and protective devices installed in transmission lines to prevent these faults but during any complex and bigger faults they require more time and also these protective devices may sometimes fails or may be damaged because excessive major faults in transmission lines. But a data driven method can be more helpful than conventional proposed methods as they need less installation cost and fast responsive and even system operators or any individual can easily identify the type of fault. This paper analyzes supervised machine learning algorithms for classifying different faults in transmission lines.
Keywords: Machine Learning, Transmission Line Faults, Data-Driven Method
Scope of the Article: Machine Learning