A METHOD FOR IDENTIFYING OPTICAL FIBER SECONDARY CIRCUITS IN INTELLIGENT SUBSTATION BASED ON CLOUD COMPUTING

Optical fiber networks are relatively complex, with issues such as time-consuming secondary loop identification and high CPU usage rates. A cloud computing based intelligent substation optical fiber secondary circuit identification method is proposed. A connectivity constraint function for generating topological structure rules is established to construct the substation optical fiber topology. Based on the topology structure, a directed graph of the substation optical fiber is drawn, and cloud computing technology is applied to perform distributed partitioning of the optical fiber topology. Following the concept of distributed storage, corresponding information for secondary loops in each area is obtained to generate labels for optical fibers in intelligent substations. A large-scale graph depth-first traversal algorithm is used to optimize the traversal process in the identification method, completing the identification process of the optical fiber secondary loops. An experimental phase is constructed, and the results show that this method has a high identification accuracy and can further reduce the time and CPU usage for secondary loop identification. It provides a new direction for the management of optical fiber networks in intelligent substations.