Imbalance Fault Detection of Direct-Drive Wind Turbines Using Generator Current Signals

Imbalance faults constitute a significant portion of all faults in wind turbine generators (WTGs). WTG imbalance fault detection using generator current measurements has advantages over traditional vibration-based methods in terms of cost, implementation, and system reliability. However, there are challenges in using current signals for imbalance fault detection due to low signal-to-noise ratio of the useful information in current signals and nonstationary characteristic frequencies of imbalance faults. This paper proposes a method of using generator stator currents for imbalance fault detection of direct-drive WTGs. In the proposed method, the variable shaft rotating frequency of a WTG is estimated from one phase stator current measured from the generator terminal by using a phase-locked loop method. The estimated shaft rotating frequency is then processed by using appropriate upsampling and variable-rate downsampling algorithms. Consequently, the variable characteristic frequencies of imbalance faults in the spectrum of the estimated shaft rotating frequency are converted to constant values. Therefore, the signatures of wind turbine imbalance faults can be clearly identified from power spectral density analysis of the converted shaft rotating frequency signal. Simulation and experimental results show that the proposed method is effective to detect various imbalance faults in direct-drive WTGs.

[1]  R.G. Harley,et al.  Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG , 2008, IEEE Transactions on Power Electronics.

[2]  Ramesh C. Bansal,et al.  IEEE Transactions on Energy Conversion , 2007 .

[3]  Y. Amirat,et al.  Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems: A Review , 2018 .

[4]  Y. Amirat,et al.  Advanced signal processing techniques for fault detection and diagnosis in a wind turbine induction generator drive train: A comparative study , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[5]  Shyh-Jier Huang,et al.  Enhancement of damage-detection of wind turbine blades via CWT-based approaches , 2006, IEEE Transactions on Energy Conversion.

[6]  Ronny Ramlau,et al.  Imbalance Estimation Without Test Masses for Wind Turbines , 2009 .

[7]  James F. Manwell,et al.  Condition monitoring and prognosis of utility scale wind turbines , 2006 .

[8]  S. A. Saleh,et al.  Development and testing of wavelet packet transform-based detector for ice accretion on wind turbines , 2011, 2011 Digital Signal Processing and Signal Processing Education Meeting (DSP/SPE).

[9]  Mohamed Benbouzid,et al.  Condition monitoring of wind turbines based on amplitude demodulation , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[10]  Ervin Bossanyi,et al.  Wind Energy Handbook , 2001 .

[11]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.

[12]  Mehrdad Moallem,et al.  The Impact of Tower Shadow, Yaw Error, and Wind Shears on Power Quality in a Wind–Diesel System , 2009, IEEE Transactions on Energy Conversion.

[13]  Vincent Choqueuse,et al.  Diagnosis of Three-Phase Electrical Machines Using Multidimensional Demodulation Techniques , 2012, IEEE Transactions on Industrial Electronics.

[14]  Wei Qiao,et al.  Simulation studies on imbalance faults of wind turbines , 2010, IEEE PES General Meeting.

[15]  Jonathon A. Chambers,et al.  Experience with bicoherence of electrical power for condition monitoring of wind turbine blades , 1998 .

[16]  A Kusiak,et al.  A Data-Driven Approach for Monitoring Blade Pitch Faults in Wind Turbines , 2011, IEEE Transactions on Sustainable Energy.

[17]  Dongxiang Jiang,et al.  Theoretical and experimental study on wind wheel unbalance for a wind turbine , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.

[18]  Jason Jonkman,et al.  FAST User's Guide , 2005 .

[19]  Minghao Zhao,et al.  Research on fault mechanism of icing of wind turbine blades , 2009, 2009 World Non-Grid-Connected Wind Power and Energy Conference.