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Fault diagnosis of wind turbine gearbox using DFIG stator current analysis

机译:基于DFIG定子电流分析的风机变速箱故障诊断。

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Gearbox faults are a leading reliability issue in wind turbines. Generator current-based methods have been successfully used in gearbox fault diagnosis and have shown advantages over the traditional vibration-based techniques in terms of implementation, cost, and reliability. This paper proposed a new generator stator current-based fault diagnostic method for the gearboxes in doubly-fed induction generator (DFIG)-based wind turbines under varying rotating speed conditions. Hilbert transform is first used to demodulate the stator current signal, and then a Vold-Kalman filter is designed to separate the nonstationary fault-related components (called the faulty signal) from the demodulated nonstationary stator current signal. Next, a synchronous resampling algorithm is designed to convert the nonstationary faulty signal to a stationary signal. Finally, the power spectral density (PSD) analysis is applied to the resampled faulty signal for the gearbox fault diagnosis. Experimental results obtained from a DFIG wind turbine drivetrain test rig are provided to verify the effectiveness of the proposed method for gearbox fault diagnosis.
机译:变速箱故障是风力涡轮机中的主要可靠性问题。基于发电机电流的方法已成功用于齿轮箱故障诊断,并且在实现,成本和可靠性方面均优于传统的基于振动的技术。提出了一种基于双馈感应发电机(DFIG)的风力发电机齿轮箱在不同转速条件下基于发电机定子电流的故障诊断方法。首先使用希尔伯特(Hilbert)变换对定子电流信号进行解调,然后设计Vold-Kalman滤波器,将非稳态故障相关分量(称为故障信号)与解调后的非稳态定子电流信号分开。接下来,设计了同步重采样算法,将非平稳故障信号转换为平稳信号。最后,将功率谱密度(PSD)分析应用于重新采样的故障信号,以进行变速箱故障诊断。从DFIG风力涡轮机传动系统测试台获得的实验结果可证明该方法对变速箱故障诊断的有效性。

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