The accurate fault-cause identification for overhead transmission lines supports the operation and maintenance personnel in formulating targeted maintenance strategies and shortening the time of inspecting faulty lines. With the goal of achieving “carbon peak and carbon neutrality”

the schemes for clean energy generation have rapidly developed. Moreover

new energy-consuming equipment has been widely connected to the power grid

and the operating characteristics of the power system have significantly changed. Consequently

these have impacted traditional fault identification methods. Based on the time-frequency characteristics of the fault waveform

new energy-related parameters

and deep learning model

this study proposes a fault identification method suitable for scenarios where a high proportion of new energy is connected to the power grid. Ten parameters related to the causes of transmission line fault and new energy connection scenarios are selected as model characteristic parameters. Further

a fault identification model based on adaptive deep belief networks was constructed

and its effect was verified by field data.