Detection Approach for Station Area Topology Change Based on Multi-Time Scale

The detection of station area topology change is a critical part of guaranteeing the accuracy of a data-driven station area topology identification algorithm, for which a detection method for station area topology change based on the multi-time scale is proposed. The detection method comprehensively constructs the topology change detection indicator mechanism with multi-source data such as voltage and current and improves the robustness of the algorithm to the error of single voltage and current data. Firstly, the transient moment comparison indicator, the adjacent moment comparison indicator, and the adjacent sliding window comparison indicator with short to long-term scales are constructed. Subsequently, a three-level station area topology change tracking and detection strategy based on multi-time scale indicators is proposed for the identification of three types of topology changes: user migration into the area, user migration out of the area, and user phase adjustment. Finally, a simulation model is built using actual user data to verify the effectiveness of the proposed algorithm, explore the performance of the proposed algorithm in different scenarios, and compare it with existing recognition algorithms. The case study shows that the proposed algorithm is effective in recognizing topology change categories and change locations compared with existing algorithms, and is robust to data measurement errors.