Online state estimation based on forecasting-correction-refinement framework for active distribution systems

The increasing penetration of distributed energy resources and new types of loads introduces rapid power fluctuations and uncertainties into active distribution systems (ADSs), posing challenges to online distribution system state estimation (DSSE) under sudden power changes. This paper develops a functionally decoupled forecasting-correction-refinement framework for online forecasting-aided DSSE. First, recursive least squares (RLS) generates one-step-ahead forecasts of the system states. The resulting forecasts are then rapidly corrected by a covariance-informed linear least-squares estimation (LLSE) stage using fixed and continuously available micro-phasor measurement unit (μPMU) voltage measurements. Subsequently, weighted least squares (WLS) uses the corrected forecasts as prior information and incorporates the remaining heterogeneous measurements to obtain the final current-slot estimates. To maintain stable recursive updating, the LLSE-corrected forecasts, rather than the final WLS estimates, are fed back to update the RLS model. Case studies on modified IEEE 34-bus and IEEE 123-bus systems demonstrate that the proposed method consistently improves state-estimation accuracy over the benchmark methods. It effectively suppresses large forecasting deviations while maintaining stable recursive operation, and exhibits satisfactory scalability and computational efficiency for online applications.