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| Enhancement of Transmission Line Protection for MASHKOR and JEBAL AWLIA Line using Artificial Neural Networks |
Ahmed Mohammed Alsayed Ibrahim(), Elebaid Khalid Elsayed(), Mohammed Hyder Musa(), Sara Sideeg Elnow Ali()
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Abstract:
in this paper, a forward artificial neural network (FANN) architecture is designed and implemented for the purpose of improving transmission line protection, particularly in Sudan, using the transmission line connecting MASHKOR and JEBAL AWLIA as a case study, which is classified as a long transmission line. The substation holding this line provides a genuine dataset for training and testing the neural network, which comprises of three phase voltages and currents at varied measured zone distances. A Simulink model for the transmission line is created, and the parameters for the line are obtained from the substation administration. The training and testing processes are then carried out, and the results demonstrate the effectiveness and powerful effects of artificial neural networks in improving protection schemes over conventional methods.
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