Aiming at the problems of complex fault mechanism and difficult to adapt to the change of topology and fault characteristics for the secondary equipment of smart substation failure

a fault location method of the secondary equipment in smart substation based on gated recurrent unit (GRU) is proposed. For the fault location objects such as merging unit

intelligent terminal

protection device

its sending and receiving network ports

and optical fibers

the alarm signals from related equipment are used when the secondary equipment fails to form an alarm signal set. Using the GRU network

each deep learning network fault location model for line

bus

and main transformer bays is established

and various training strategies for the secondary equipment fault location model are given. Through the case of a typical smart substation

and the effectiveness and accuracy of the proposed fault location method are verified by simulation experiments

and compared with the long short-term memory network and recurrent neural network

the proposed method can be more accurately and rapidly to locate secondary equipment in smart substation.