Abstract: In order to timely and accurately detect grounding faults in wind farm AC transmission lines and classify them. At the same time,to solve the influence of fault resistance,fault starting time and fault location on fault diagnosis accuracy,a fault detection and classification method for wind turbine AC transmission lines based on the combination of Clarke transform,discrete wavelet transform（DWT）and feedforward neural network（FFNN）is proposed. This method decomposes and generates the measured three-phase voltage signal through Clarke transform γ component,α component and β component,DWT is used to extract high-frequency components from three components,and five statistical methods are applied to high-frequency component D1to generate the final fault feature values. Take γ component of fault eigenvalues and γ Component,α component and β component of fault eigenvalues are combined with FFNN to achieve accurate detection and classification of wind turbine grounding faults. Through 1400 fault cases,it has been verified that this method can effectively diagnose faults in wind farm AC transmission lines without being affected by fault resistance,fault initiation time,and fault location.