An extreme gradient boosting (XGBoost) method for photovoltaic array fault diagnosis is proposed to solve the problems of low precision and poor model performance due to the nonlinear output of photovoltaic array and the influence of maximum power point tracking. Firstly, based on the single diode model of photovoltaic cell, a simulation model of photovoltaic array was established, and the output characteristics and fault causes of photovoltaic array were systematically simulated and analyzed by using PVsyst software, and the fault characteristic parameters were obtained and the validity of the selected fault feature parameters is verified by the feature importance ranking. Secondly, the fault characteristics of photovoltaic array under different fault states are extracted, and the fault diagnosis model based on XGBoost is constructed. Finally, grid search and cross validation were used to optimize the hyperparameters of the diagnostic model, and the performance of the diagnostic model was evaluated by confounding matrix calculation. Compared with decision tree, random forest and gradient lifting tree, the results show that the proposed method not only can accurately detect all kinds of faults, but also has better generalization ability and higher diagnosis accuracy.