In recent years,the transmission line faults caused by birds in Guangdong power grid have been increasing gradually,which have become one of the main hidden dangers for power grid security.How to reduce the bird damage has become a new topic of transmission line operation and maintenance.For the widely distributed transmission lines,it is difficult to effectively prevent bird damage by driving birds approach.Here,based on the operation and maintenance data related with bird damage on Guangdong power grid from 2015 to 2019,we analyzed the geographical characteristics,pole tower structures and seasons,then established a model to analyze the transmission line faults caused by bird damage.First,the influence of geographical characteristics,pole tower structure and season on bird caused fault is analyzed.Then,a Mask R-CNN neural network is trained to extract the geographical characteristics around the pole tower.Finally,a bird damage fault model based on Bayesian linear regression is established,and the accuracy of the model is evaluated by the correlation coefficient of R².The experimental results show that the model has high accuracy and reliability.