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Omer Elfaki Elbashir
North China Electric Power University

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Analysis of DFIG Wind Turbine During Steady-State and Transient Operation Omer Elfaki Elbashir; Wang Zezhong; Liu Qihui
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

In recent years, there has been a worldwide growth in the exploitation of wind energy. In the wind power industry, the majority of grid-connected wind turbines are equipped with doubly fed induction generators (DFIGs) because of their advantages over other wind turbine generator (WTG) systems. Therefore, much research effort has gone into the issues of modeling, analysis, control and grid integration of DFIG wind turbines. This paper deals with the modeling, analysis, and simulation of a DFIG driven by a wind turbine. The grid connected wind energy conversion system (WECS) is composed of DFIG and two back to back PWM voltage source converters (VSCs) in the rotor circuit. A machine model is derived in an appropriate reference frame. The grid voltage oriented vector control is used for the grid side converter (GSC) in order to maintain a constant DC bus voltage, while the stator voltage oriented vector control is adopted in the rotor side converter (RSC) to control the active and reactive powers. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.5205
Condition Monitoring and Faults Diagnosis for Synchronous Generator Using Artificial Neural Networks Omer Elfaki Elbashir; Wang Ze Zhong; Liu Qi Hui
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: February 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Early detection and diagnosis of incipient fault is desirable for on line condition assessment production quality assurance and improved operational efficiency of synchronous generator running of power supply. Artificial Intelligent techniques are increasly used for condition monitoring and fault diagnosis of machines. In this paper, Artificial Neural Network (ANN) approach employed for fault diagnosis in the generator, based on monitoring generator currents to give indication of the winding faults. Feed-forward Network, error back propagation training algorithm are used to perform the generator faults diagnosis and their values. NN which has been trained for all possible operating condition of the machine used to classify the incoming data. The inputs of the NN are the stator and rotor currents, and the output represents the running condition of the generator. The training of the NN achieved by the data through a mathematical model based approach to simulate the generator faults at various degree of severity.This paper evaluates through simulation line currents magnitude of the generator .The final results have been represented on a monitoring unit, built using matlab program, to give early warning of the generator failure. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3809
Co-Authors Liu Qi Hui Liu Qihui Wang Ze Zhong Wang Zezhong