Enhancement of Turbo-Generators Phase Backup Protection Using Adaptive Neuro Fuzzy Inference System

Mohamed Salah El-Din Ahmed Abdel Aziz, Mohamed Elsamahy, Mohamed A. Moustafa Hassan and Fahmy M. A. Bendary
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Mohamed Salah El-Din Ahmed Abdel Aziz: Dar Al-Handasah (Shair and Partners), Giza, Egypt
Mohamed Elsamahy: Electrical Power Department, The Higher Institute of Engineering, El-Shorouk Academy, El-Shorouk City, Egypt
Mohamed A. Moustafa Hassan: Electrical Power Department, Faculty of Engineering, Cairo University, Giza, Egypt
Fahmy M. A. Bendary: Electrical Power Department, Faculty of Engineering at Shoubra, Benha University, Banha, Egypt

International Journal of System Dynamics Applications (IJSDA), 2017, vol. 6, issue 1, 58-76

Abstract: This research work presents an advanced solution for the problem due to the current setting of Relay (21). This problem arises when it is set to provide thermal backup protection for the generator during two common system disturbances, namely a system fault and a sudden application of a large system load. These investigations are carried out using Adaptive Neuro Fuzzy Inference System (ANFIS). The results of the investigations have shown that the ANFIS has a promising tool when applied for turbo-generators phase backup protection. The effect of this tool varies according to the type of input data used for ANFIS testing and validation. The proposed method in this paper proposes the use of two different sets of inputs to the ANFIS, these inputs are the generator terminal impedance measurements (R and X) and the generator three phase terminal voltages and currents (V and I). The dynamic simulations of a test benchmark have been conducted using the PSCAD/EMTDC software. The results obtained from the ANFIS scheme are encouraging.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jsda00:v:6:y:2017:i:1:p:58-76

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