Recurrent Neural Networks Based Fault Detection for Synchronous Generator Stator Windings Protection.
Helal, A., El Dessouki, H., Hatata, A., El-Saadawi, M., Tantawy, M. (2020). Recurrent Neural Networks Based Fault Detection for Synchronous Generator Stator Windings Protection.. EKB Journal Management System, 38(1), 1-13. doi: 10.21608/bfemu.2020.115397
A. Helal; H. El Dessouki; A. Hatata; Magdi Mohamed Ali El-Saadawi; Mohamed Abd El-Moneim Mohamed Tantawy. "Recurrent Neural Networks Based Fault Detection for Synchronous Generator Stator Windings Protection.". EKB Journal Management System, 38, 1, 2020, 1-13. doi: 10.21608/bfemu.2020.115397
Helal, A., El Dessouki, H., Hatata, A., El-Saadawi, M., Tantawy, M. (2020). 'Recurrent Neural Networks Based Fault Detection for Synchronous Generator Stator Windings Protection.', EKB Journal Management System, 38(1), pp. 1-13. doi: 10.21608/bfemu.2020.115397
Helal, A., El Dessouki, H., Hatata, A., El-Saadawi, M., Tantawy, M. Recurrent Neural Networks Based Fault Detection for Synchronous Generator Stator Windings Protection.. EKB Journal Management System, 2020; 38(1): 1-13. doi: 10.21608/bfemu.2020.115397

Recurrent Neural Networks Based Fault Detection for Synchronous Generator Stator Windings Protection.

MEJ- Mansoura Engineering Journal
Article 6, Volume 38, Issue 1, March 2013, Page 1-13  XML PDF (17.15 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.115397
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Authors
A. Helal* 1; H. El Dessouki1; A. Hatata2; Magdi Mohamed Ali El-Saadawi3; Mohamed Abd El-Moneim Mohamed Tantawy4
1Electrical Engineering and Control Department., Faculty of Engineering., Arab Academy for Science and Technology., Alex. Egypt.
2Electrical Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
3Professor of Electrical Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
4Professor of Electrical Engineering Department., Faculty of Engineering., Mansoura University., Mansoura., Egypt.
Abstract
This paper presents a proposed approach for fault detection and faulty phase(s) identification for synchronous generator protection-based om artificial neural networks. In order to perform this approach: the protection system is subdivided into different neural network modules for fault detection and classification. The proposed approach uses Recurrent Neural Network (RNN) to detect and classify the synchronous generator internal faults. The RNN uses the three-phase current measurements from both sides of the synchronous generator stator winding as its input data. RNN was trained using various sets of data available from the simulation results of the selected synchronous model under different fault scenarios (fault type, fault location, fault resistance and fault inception angle). Simulation results of the proposed RNN based synchronous generator stator winding protection provide a great performance; in terms of accuracy, speed and reliability.
Keywords
synchronous generator; Differential protection; Recurrent Neural Networks; fault detection; fault classification
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