The Effect of Alpha Oscillation Network Decoding on Driver Alertness

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

Chi Zhang
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Jinfei Ma
School of Psychology, Liaoning Normal University, Dalian 116029, China.

Jian Zhao
Faculty of Vehicle Engineering and Mechanics, School of Automative Engineering, Dalian University of Technology, Dalian 116024, China.

Pengbo Liu
Faculty of Vehicle Engineering and Mechanics, School of Automative Engineering, Dalian University of Technology, Dalian 116024, China.

Fengyu Cong
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China and School of Artificial Intelligence, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China and Key Laboratory of Integrated Circuit and Biomedical Electronic System, Liaoning Province. Dalian University of Technology, Dalian, China and Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland.

Tianjiao Liu
School of Psychology, Shandong Normal University, Jinan 250358, China.

Ying Li
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Lina Sun
Faculty of Electronic Information and Electrical Engineering, School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

Ruosong Chang
School of Psychology, Liaoning Normal University, Dalian 116029, China.

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