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{179122,
        author = {Vipin Dubey and Raghunandan Singh Bhagel},
        title = {A Review paper on fault detection using artificial neural networks in HVDC transmission systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5462-5466},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179122},
        abstract = {The reliable functioning of AC-DC systems hinges on prompt monitoring and precise classification of system signals. For fast-acting HVDC transmission systems, decisions often need to be made within tens of milliseconds to prevent disturbances like commutation failures. Effective fault detection and clearance are vital for optimal power system operation. Detecting faults in HVDC systems is a significant challenge. Traditional methods, such as pure frequency or time-domain analysis, have limitations. Frequency-domain methods struggle with time-varying transients, while time-domain methods are susceptible to noise. Recent advancements in power electronics and artificial intelligence (AI) have transformed fault detection in HVDC systems. AI techniques like fuzzy logic, neural networks, and artificial neural networks (ANNs) have shown promise in identifying faults. This overview explores the application of AI techniques for fault detection in HVDC transmission systems, highlighting their potential to enhance system reliability and efficiency.},
        keywords = {ANN, Fuzzy, Wavelet Transformation, Fault Identification, Genetic Algorithms.},
        month = {May},
        }