Skewness Driven DFIG's DC Link Bus Short and Open Circuit Faults Diagnosis Using Rotor and Stator Currents

DC link bus plays an important role in the operation of doubly fed induction generators (DFIG) and sustainable power generation. It is used in the converter section connected between the rotor side converter (RSC) and stator side converter (SSC). Here a Skewness-driven deep learning method has been proposed for DFIG's DC link bus fault diagnosis using rotor current as well as stator current. Study shows that harmonics in both rotor and stator currents are influenced by faults. Stator and rotor current signature Wavelet decomposition-based Skewness-driven statistical scanning has been made to disintegrate normal, short, and open circuit fault conditions incorporating an artificial neural network (ANN) based algorithm. The method has been first applied in a simulated environment and then testified in real fault conditions. The results obtained are satisfactory for effective fault identification from the rotor as well as from the stator sides.