Loss of Excitation Faults Detection in Hydro-Generators Using an Adaptive Neuro Fuzzy Inference System

Aziz, Mohamed Salah El-Din Abdel; Elsamahy, Mohamed; Moustafa, Mohamed; Bendary, Fahmy

doi:10.11591/ijeecs.v1.i2.pp300-309

Cited by 10 publications

(1 citation statement)

References 19 publications

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“…Adaptive Neuro-Fuzzy networks are enhanced FLSs with learning, generalization, and adaptive capabilities. These networks encode the fuzzy if-then rules into a neural network-like structure and then use appropriate learning algorithms to minimize the output error based on the training/validation data sets Aziz 2012;Aziz 2012;Kamel 2011;Kamel 2012;Azar, 2010a;Azar, 2010b;Azar, 2012;Azar and Serrano, 2015a;Azar and Serrano, 2015b;Azar and Serrano, 2015c;Aziz 2016;Aziz 2016a;Aziz 2016b;Aziz 2016c). Neuro-adaptive learning techniques provide a method for the fuzzy modeling procedure to learn information about a data set.…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

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

Aziz

Elsamahy

Hassan

et al. 2017

International Journal of System Dynamics Applications

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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.

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Section: Adaptive Neuro Fuzzy Inference System (Anfis)mentioning

confidence: 99%

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

Aziz

Elsamahy

Hassan

et al. 2017

International Journal of System Dynamics Applications

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Discriminating Between Loss of Excitation and Power Swings in Synchronous Generator Based on ANN

Barakat

Hajjar

Kherbek

et al. 2019

J Control Autom Electr Syst

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This paper presents a newly designed scheme based on neural networks to detect loss of excitation (LOE) in synchronous generators. The proposed scheme uses more accurate mechanism and needs fewer parameters in order to achieve fast and reliable detection of LOE. Furthermore, being able to discriminate between LOE and stable power swings is a major concern to enhance the performance of traditional LOE protection. Therefore, the designed network is trained to discriminate between both cases clearly. For training and testing the proposed neural network, MATLAB program has been used for simulation. In addition, by using comparison analysis between the designed network and the previous ones and the traditional MHO relay, the results ensure that the proposed scheme has more secure and fast characters in detecting and discriminating LOE. Keywords Synchronous generator • Loss of excitation • Power swing • Protection • Neural networks • Artificial neural network • Stable power swings List of Symbols X s The system impedance X d The synchronous reactance of the d-axis X ′ d The transient reactance of the d-axis X ′′ d The sub-transient reactance of the d-axis X ′ q The transient reactance of the q-axis X ′′ q The sub-transient reactance of the q-axis T do ′ The d-axis transient open-circuit time constant T do ′′ The d-axis sub-transient open-circuit time constant T qo ′ The q-axis transient open-circuit time constant I Generator's current V Generator's voltage Abbreviations AI Artificial intelligence ANFIS Adaptive neuro-fuzzy inference system ANN Artificial neural network FACTS Flexible AC transmission systems LOE Loss of excitation

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Detecting loss of excitation condition of synchronous generator in the presence of unified power flow controller based on data mining method

Rahmkhoda,

Faiz,

Abedini

2024

Electric Power Systems Research

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Loss of Excitation Faults Detection in Hydro-Generators Using an Adaptive Neuro Fuzzy Inference System

Abstract: This paper presents a new approach for Loss of Excitation (LOE) faults detection in

Cited by 10 publications

References 19 publications

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

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

Discriminating Between Loss of Excitation and Power Swings in Synchronous Generator Based on ANN

Detecting loss of excitation condition of synchronous generator in the presence of unified power flow controller based on data mining method

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