<span>In </span><span>the current paper, a method is developed to diagnose potential electrical defects attacking doubly fed induction generator in Moroccan wind turbines firms. The proposed method is based on stator and rotor currents Lissajous curves analysis. Firstly, we focus on modeling of a non-defected wind conversion systems based on mathematic model created in Matlab Simulink which is able to reflect the behaviour of the wind turbine during asynchronous generator defects-free operation. After that, an indirect stator field vector oriented control is applied to obtain the wind system performance. Finally, stator and rotor currents Lissajous curves are analyzed in case of a non-defected generator that represents the system reference curves for diagnosing defects. The simulations had been realized by Matlab Simulink. Their results showed the effectiveness of the proposed method.</span>

In this research paper, we will present the first works related to a state of the art and assess different defects found in two Moroccan wind turbine parks.In this respect, it is of paramount importance to shed light on these wind turbine parks and conduct a scientific research to analyze and come up with certain findings related to the issue under study.We are going to focus on the main defects that occur in wind turbines and thus we will sort out the most frequent ones in an attempt to provide an overall analysis of the system and its defects.

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The research consists on developing method to diagnose electrical defects affecting wind turbine doubly-fed induction generator DFIG which constitutes a crucial part of wind energy conversion chain. First off all, we create a model of a non-defected wind conversion system based on mathematical equations introduced in Matlab Simulink. Then, we apply an indirect vector control stator field orientation in order to increase wind energy performance. With the aim of diagnosing the defects attacking wind turbine generator, we propose a method based on grouping of fast Fourier transform spectral analysis and Lissajous curves performed to generator stator and rotor currents. This diagnosis technique is applied to wind turbine in normal operation (non-defected generator) in order to have a reliable reference data for asynchronous generator behaviour. However, connected to the grid, wind turbine generator is affected by various faults occurring in electrical power networks. Therefore, the diagnosis method is applied also to a defected generator. Considering diversity of grid defects, we deal in the current paper with open stator supplying phases and open rotor feeding phases due to rotor side converter legs opening. Indeed, this diagnosis method allows diagnosing generator defects type and severity by comparing the resulting frequency spectrum analysis and Lissajous curves under abnormal condition operating to reference data obtained in case of non-defected generator. So, our proposed method contributes to DFIG defects identification and anticipation. The simulations had been accomplished using Matlab Simulink. These results proved the efficiency and effectiveness of the proposed DFIG diagnosis method for wind energy conversion chain.