Recent Development of Intelligent Shunt Fault Classifier for Nigeria 33-kV Power Lines
This research describes a novel way to employing artificial neural networks (ANNs) to improve transmission line protection. The suggested technique feeds four different neural network structures instantaneous voltages and currents on a transmission line during normal and fault conditions. The structures are then expertly merged to provide a system that can more effectively detect and diagnose shunt problems. The report goes into great detail about the design process as well as the many simulations that were run. The accuracy and mean square error (MSE) of the created system are examined, and the findings reveal that this approach is capable of identifying and classifying all probable shunt faults on the 33-kV Nigeria power lines in less than 1ms with a high level of precision. When evaluated under various shunt fault types with varying resistances and distances, the system’s performance demonstrates that it can be used to improve distance line protection in 33-kV Nigeria power lines.
Author (s) Details
A. A. Awelewa
Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State, Nigeria.
P. O. Mbamaluikem
Department of Electrical/Electronics Engineering, Federal Polytechnic Ilaro, Ilaro, Ogun State, Nigeria.
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