Abstract: The real-time and synchronization of power fiber optic communication networks directly affect the efficiency of the entire power grid communication network. Unreasonable handling of the timing characteristics of power fiber optic communication may lead to collaboration issues between power grid equipment. This paper aims to achieve high-precision determination of time signals based on the wavelength of fiber optic transmission. Firstly， the differential autoregressive integrated moving average mode （ARIMA） is used to sequence the input fiber optic data and calculate the wavelength and transmission speed to accurately predict the nodes for fixed time data transmission. Secondly， extreme learning machines algorithm （ELM） is applied to extract and analyze spatiotemporal features of data， ensuring that data from different sources can maintain synchronization when input into optical fibers， achieving dual synchronization in both time and space. Finally， the data transmission node is determined based on the transmission wavelength of the optical fiber. The experimental analysis results indicate that fiber optic data transmission can achieve high-quality full-time synchronization between power grid control equipment and the dispatch and command center.

Key words:  , power fiber optic communication network, ARIMA, ELM, full time synchronization