Substation secondary-circuit monitoring data matching and mapping based on a graph-theory-based depth-first traversal algorithm

Traditional monitoring methods are often characterized by low precision and delayed data acquisition, rendering them incapable of meeting the requirements for real-time monitoring and analysis of substation equipment. To address this, a depth-first traversal algorithm based on graph theory is designed to map monitoring data in secondary circuits of substations. A comprehensive data acquisition architecture is constructed, encompassing both field devices and communication networks, and a virtual environment model is established through three-dimensional data collection. Online monitoring data are clustered using the clustering by fast search and find of density peaks algorithm, which integrates multiple state information of the substation. The physical links of secondary circuits within the substation are traversed using a graph-based depth-first search, and the connectivity of input and output devices is analyzed based on tree matching principles. A multimodal matching model is built to dynamically map inventory and incremental models, enabling real-time updates of data and mappings. The implemented approach achieves a coverage rate of over 80% with a response time of only 6.19 ms. The mapping precision exceeds 85% and can reach up to 98%.