Photovoltaic power stations are mostly located in harsh environment and are prone to various types of failures. At present

there are great difficulties in accurate fault identification and location. For this reason

a fault identification and location strategy for photovoltaic power stations based on gated cycle network(GRU) is proposed.Firstly

the simulation model of photovoltaic power station is established

and the disturbance observation method is applied to realize the maximum power tracking of the photovoltaic modules. Via simulation tests for different types of photovoltaic array faults

the fault characteristic parameters are determined. Then

the sample sets of different faults are collected

and the fault diagnosis model based on the GRU neural network is established

which is verified by the simulation model and photovoltaic experimental platform. The verification results show that

the GRU neural network fault diagnosis model can accurately identify and locate photovoltaic faults

which can be used to guide the daily maintenance of photovoltaic power stations and improve the economic benefits of photovoltaic power stations.