Neural Network Based Fault Detector and Classifier for Synchronous Generator Stator Windings.
Hatata, A., Helal, A., El Dessouki, H., El-Saadawi, M., Tantawy, M. (2020). Neural Network Based Fault Detector and Classifier for Synchronous Generator Stator Windings.. EKB Journal Management System, 36(4), 19-28. doi: 10.21608/bfemu.2020.122340
Ahmad Hatata; Ahmed Helal; Hesien El Dessouki; Magdi Mohamed Ali El-Saadawi; Mohammed Tantawy. "Neural Network Based Fault Detector and Classifier for Synchronous Generator Stator Windings.". EKB Journal Management System, 36, 4, 2020, 19-28. doi: 10.21608/bfemu.2020.122340
Hatata, A., Helal, A., El Dessouki, H., El-Saadawi, M., Tantawy, M. (2020). 'Neural Network Based Fault Detector and Classifier for Synchronous Generator Stator Windings.', EKB Journal Management System, 36(4), pp. 19-28. doi: 10.21608/bfemu.2020.122340
Hatata, A., Helal, A., El Dessouki, H., El-Saadawi, M., Tantawy, M. Neural Network Based Fault Detector and Classifier for Synchronous Generator Stator Windings.. EKB Journal Management System, 2020; 36(4): 19-28. doi: 10.21608/bfemu.2020.122340

Neural Network Based Fault Detector and Classifier for Synchronous Generator Stator Windings.

MEJ- Mansoura Engineering Journal
Article 13, Volume 36, Issue 4, December 2011, Page 19-28  XML PDF (13.98 MB)
Document Type: Research Studies
DOI: 10.21608/bfemu.2020.122340
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Authors
Ahmad Hatata* 1; Ahmed Helal2; Hesien El Dessouki3; Magdi Mohamed Ali El-Saadawi4; Mohammed Tantawy5
1Electrical Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
2Assistant Professor., Electrical and Control Engineering Department., Faculty of Engineering., Arab Academy for Science and Technology., Alex., Egypt.
3Dept of Elec. Engineering and Control, Faculty of Engineering, Arab Academy for Science and Technology, Alex. Egypt.
4Professor of Electrical Engineering Department., Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
5Professor of Electrical Engineering Department, Faculty of Engineering., El-Mansoura University., Mansoura., Egypt.
Abstract
This paper presents an application of multilayer feedforward neural network (MFNN) as a differential protection for synchronous generators. Two MFNN are designed, trained, and tested in this paper. The first one has two outputs which detect the internal and external fault state. The other neural network has four outputs to classify the faulty phases. The proposed neural fault detector and classifier were trained using various sets of data available from a selected synchronous model and simulating different fault scenarios (fault type, fault location, fault resistance and fault inception angle). The results show very good behavior of the MFNN and it was more reliable and accurate than conventional methods. It shows that MFNN offer the possibility to be used for on line synchronous generator protection and give satisfactory results. 
Keywords
Differential protection; Generator protection; Multilayer feedforward neural networks; Fault detector and classification
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