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Online nonintrusive condition monitoring and fault detection for wind turbines
The goal of this dissertation research is to develop online nonintrusive condition monitoring and fault detection methods for wind turbine generators (WTGs). The proposed methods use only the current measurements that have already been used by the control and protection systems of WTGs; no additional sensors or data acquisition devices are needed. Current-based condition monitoring and fault detection techniques have great economic benefits and the potential to be adopted by the wind energy industry. However, there are challenges in using current measurements for wind turbine condition monitoring and fault detection. First, it is a challenge to extract WTG fault signatures from nonstationary current measurements, due to variable-speed operating conditions of WTGs. Moreover, the useful information in current measurements for wind turbine condition monitoring and fault detection usually has a low signal to noise ratio, which makes the condition monitoring and fault detection difficult. WTG faults can be classified into two categories: the faults with characteristic frequencies (i.e., Type 1 faults) and the faults without characteristic frequencies (i.e., type 2 faults). For type 1 faults, appropriate demodulation methods have been proposed to calculate the frequency and the amplitude of the current measurements. Two 1P-invariant power spectrum density (PSD) method have then been proposed to use appropriate resampling algorithms to convert the variable characteristic frequencies of WTG faults in the frequency domain of the current demodulated signals to constant values, where 1P stands for the shaft rotating frequency of the WTG. An impulse detection method has then been designed to find out the excitations in the 1P-invariant PSD of the current demodulated signals, where the excitations at the characteristic frequencies of WTG faults are extracted as the fault signatures. Finally, a fault signature evaluator has been designed to evaluate the WTG condition for fault detection. For Type 2 faults, a wavelet filter-based method has been developed to generate the fault index, which is then evaluated by a statistical control method-based fault index evaluator for fault detection. The proposed methods have been validated by extensive computer simulations and experiments for small direct-drive WTGs.
Gong, Xiang, "Online nonintrusive condition monitoring and fault detection for wind turbines" (2012). ETD collection for University of Nebraska - Lincoln. AAI3546799.