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Distribution system state estimation (DSSE) is a critical analysis tool for active distribution networks (DNs). Unlike weighted least squares techniques, which are static DSSE methods, the augmented complex Kalman filter (ACKF) is a novel technique that considers the system&#x2019;s dynamic behavior. Currently, most DNs integrate a large number of unmonitored residential photovoltaic (PV) generations. Existing unmeasured PV sources violate the white noise assumption in Kalman and least-squares-based estimators, causing the estimator to be biased. Because the one-step difference of aggregated customer demand is characterized as white noise, the suggested PV estimation technique based on the differencing strategy is used to decouple PV from the measured load. Using the specified contribution factors, the new online pseudo current injections are generated. In addition, the estimator&#x2019;s accuracy is improved by using a new PV-scaling-aided ACKF approach based on the PV separation strategy. For validation purposes, this method is applied to real DN case studies. This study also makes use of an actual dataset to illustrate the efficacy of the proposed technique. The proposed technique outperforms the existing snapshot and dynamic DSSE techniques, and significant improvements are achieved in terms of accuracy and computational cost.

Distribution System State Estimation Using PV Separation Strategy in LV Feeders With High Levels of Unmonitored PV Generation