In order to solve the problem that it is difficult to locate accurately after short-circuit in the collection line of hybrid connection of wind farm collection line, a fault location method based on improved depth AutoEncoder network is presented.By analyzing the zero sequence current on power line faults, it is known that the transient current value, steady-state current amplitude, steady-state current phase and fault distance are strongly non-linear, and the precise location of power line is achieved by deep learning mining this complex relationship.A distance regression output port is added to the deep denoising self-coding framework, and joint training is used to improve the accuracy, noise resistance and robustness of the positioning network.The process is as follows: Firstly, the hub model is built with PSCAD/EMTDC, and fault zero sequence current sequence and corresponding distance in a given time window are used as fault samples to simulate different failure cases to generate sample sets.Then, an improved depth AutoEncoder network is trained on the training set to obtain an optimal network for precisely measuring the fault distance.With the help of the zero sequence current amplitude relationship of each measurement point, the fault area can be determined first, and the precise location of the fault can be determined by feeding the fault samples into the trained network.This method has a good adaptability to multi-branch and hybrid short lines of power collector.Location performance is significantly better than traditional machine learning algorithms, and is less affected by transition resistance, sampling rate, noise, fault phase angle.