Linear State Estimation for Real-Time Monitoring of Active Distribution Networks

The rapid integration of distributed energy resources (DERs) and smart technologies has transformed distribution networks into active systems, creating new monitoring and control challenges. Conventional state estimation methods for transmission networks are not suited to active distribution networks (ADNs), which often feature unbalanced loads, weakly meshed structures, and diverse measurements. This thesis develops and validates a novel linear distribution network state estimation (DNSE) framework for fast and robust state estimation and anomaly detection in ADNs. The proposed method improves computational efficiency and scalability by directly relating hybrid measurements—including real-time and delayed smart meter data—to system states using linear equations, eliminating model approximations. Four solution methods are introduced, offering trade-offs between accuracy and computational burden. Both static least squares and dynamic Kalman filtering approaches are used, with dynamic DNSE enabling pre-estimation anomaly detection. A unified anomaly detection mechanism utilises the innovation vector and robust Mahalanobis distance to distinguish real events from measurement errors. Simulation results on IEEE test feeders demonstrate superior accuracy and speed compared to established nonlinear methods, even under challenging scenarios. This research advances DNSE for ADNs, supporting grid operators in managing DER-rich networks and enhancing operational efficiency.