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• W4319001479 endingPage "e13376" @default.
• W4319001479 startingPage "e13376" @default.
• W4319001479 abstract "One of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults detection and localisation to attain accuracy, precision and speed of execution. A 330 kV, 500 km three-phase transmission line was modelled to extract faulty current and voltage data from the line. The Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 μs of detection and an average error of 0%–0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. This proposed model serves as the basis for transmission line fault protection and management system." @default.
• W4319001479 created "2023-02-03" @default.
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• W4319001479 date "2023-02-01" @default.
• W4319001479 modified "2023-10-18" @default.
• W4319001479 title "The use of artificial neural network for low latency of fault detection and localisation in transmission line" @default.
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• W4319001479 doi "https://doi.org/10.1016/j.heliyon.2023.e13376" @default.
• W4319001479 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36816249" @default.
• W4319001479 hasPublicationYear "2023" @default.
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