Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{152464,
        author = {Suchita Ramesh Gabhane and Dr.Swapnil.B.Mohod},
        title = {MACHINE LEARNING FRAMEWORK FOR DETECTION, CLASSIFICAITION AND ZONE LOCATION OF FAULT IN TRASMISSION LINE},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {479-486},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152464},
        abstract = {For reliable and high-speed protective relaying, fault classification is required, followed by digital distance protection. As a result, a thorough examination of these procedures is required. The vast array of power systems and applications necessitates the development of appropriate fault classification algorithms in power transmission systems in order to improve system efficiency and avert catastrophic damage. Using Machine Learning methods, this project suggested an effective methodology for detecting faults, classifying fault types, and locating fault zones in transmission lines, which could be applied in numerical relays. The entire system is capable of detecting no-fault conditions, three types of line-to-ground faults, line-to-line faults, and double line-to-ground faults, as well as signaling the fault zone. Each transmission line has been divided into three zones to help find the fault. With the aid of confusion matrix parameters, the algorithm is constructed and analyzed in MATLAB.},
        keywords = {Fault Detection, Fault Classification, Transmission Lines, Machine Learning},
        month = {},
        }