Rolling Re-Synchronization of Low-Voltage Load Measurements by Integrating Physical Laws and Data Analytics

Load measurements are commonly asynchronous in a low-voltage distribution network, which pose significant challenges to the situational awareness of distribution system operations, thereby hampering the timeliness and effectiveness of potential control actions. To solve this issue, this article develops a re-synchronization method to dynamically correct asynchronous load measurements on a rolling basis for three-phase unbalanced low-voltage distribution networks. First, we utilize the idea of low rank matrix recovery to formulate the objective function, and derive a set of constraints based on both the physical mechanism and data analytics. More specifically, the physical mechanism like Kirchhoff's current and voltage laws are applied to characterize the inherent relations among load measurements, while historical power usage patterns of consumers are analyzed to impose data-driven constraints. The proposed model is by nature a convex optimization problem that can be solved in an efficient way. Additionally, deployment suggestions for additional synchronous measuring devices are stated to further enhance the synchronism among load measurements. Simulation results demonstrate the superior performance of the proposed method in enhancing the synchronism among load measurements, even under serious asynchronous conditions.