Stator fault analysis based on wavelet technique for wind turbines equipped with DFIG

Abstract

The paper introduces a monitoring and diagnostic technique for the detection of incipient stator electrical faults in doubly fed induction generators (DFIGs) for wind power systems. Depending on wind speed, the induction machine operates continuously in non stationary conditions. In this context, traditional Fourier Analysis fails to discriminate between healthy and abnormal stator operating conditions. To overcome this limitation a wavelet based analysis of rotor currents is here proposed in order to detect stator faults. This technique allows extracting fault frequencies dynamically over time providing effective fault detection. Moreover the mean power at different resolution levels was introduced as a diagnostic index to quantify the extent of the fault. Simulation and experimental results show that wavelet decomposition allows good discrimination between healthy and faulty cases even in time-varying conditions leading to an effective diagnostic procedure for stator faults in DFIG thereby.