Wind Turbine Doubly-Fed Asynchronous Machine Diagnosis Defects -Part two

Hammouchi, Fatima El; Menzhi, Lamia El; Saâd, Abdallah; Ihedrane, Yasmine; Bossoufi, Badre

doi:10.1109/cist.2018.8596498

Cited by 2 publications

(4 citation statements)

References 5 publications

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“…For minimizing downtime, predicting in time imminent failures and improving maintenance schedule, several techniques are applied for monitoring continuously the wind energy conversion system health [23]. Indeed, online diagnosis defects allows an early detection of the structure health degradation and prevents incipient faults in the wind energy conversion system which leads to save both money and time.…”

Section: Review Of Diagnosis Defects Essential Methodsmentioning

confidence: 99%

“…Indeed, online diagnosis defects allows an early detection of the structure health degradation and prevents incipient faults in the wind energy conversion system which leads to save both money and time. Multitude of internal and external defects attacks frequently wind turbines once its start working [24] especially the generator. Indeed, DFIG undergoes some common defects such as generator excessive vibration, generator overheating, bearing overheating, abnormal noises and insulation damage, etc [25].…”

Section: Review Of Diagnosis Defects Essential Methodsmentioning

confidence: 99%

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Wind turbine doubly-fed asynchronous machine diagnosis defects using stator and rotor currents lissajous curves

Hammouchi¹,

Menzhi²,

Saâd³

et al. 2019

IJPEDS

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<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>

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Section: Review Of Diagnosis Defects Essential Methodsmentioning

confidence: 99%

Section: Review Of Diagnosis Defects Essential Methodsmentioning

confidence: 99%

Wind turbine doubly-fed asynchronous machine diagnosis defects using stator and rotor currents lissajous curves

Hammouchi¹,

Menzhi²,

Saâd³

et al. 2019

IJPEDS

Self Cite

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“…However, faults in electrical power networks are inevitable events. According to data collected from yearly defects study of wind turbines Moroccan park [14], wind turbine connected to grid is exposed to various grid faults: 22% of defects are due to grid faults as presented in figure 13 (a). Also, grid defects can cause significant losses to wind system even if grid defects frequency remains less than other defects frequencies figure 13 (b) .…”

Section: Dfig Diagnosis Defects Methodsmentioning

confidence: 99%

“…We introduced sum of different sinusoidal signals: varied amplitudes and frequencies in order to create wind speed model in Matlab Simulink as showed in the figure 2, which reflects approximately a recorded wind speed in Moroccan wind farms [10]. We assume that frictions are neglected and with the use of equations presented in previous paper [11], the wind turbine Matlab Simulink model is created.…”

Section: Wind Turbine Modelingmentioning

confidence: 99%

DFIG Defects Diagnosis Method for Wind Energy Conversion Chain

Hammouchi¹,

Menzhi²,

Saâd³

2019

Adv. sci. technol. eng. syst. j.

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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.

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Wind Turbine Doubly-Fed Asynchronous Machine Diagnosis Defects -Part two

Cited by 2 publications

References 5 publications

Wind turbine doubly-fed asynchronous machine diagnosis defects using stator and rotor currents lissajous curves

Wind turbine doubly-fed asynchronous machine diagnosis defects using stator and rotor currents lissajous curves

DFIG Defects Diagnosis Method for Wind Energy Conversion Chain

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