  Traditional power flow methods often adopt certain assumptions designed for
passive balanced distribution systems, thus lacking practicality for unbalanced
operation. Moreover, their computation accuracy and efficiency are heavily
subject to unknown errors and bad data in measurements or prediction data of
distributed energy resources (DERs). To address these issues, this paper
proposes a hybrid data-aided robust power flow algorithm in unbalanced
distribution systems, which combines Taylor series expansion knowledge with a
data-driven regression technique. The proposed method initiates a linearization
power flow model to derive an explicitly analytical solution by modified Taylor
expansion. To mitigate the approximation loss that surges due to the DER
integration and bad data, we further develop a data-aided robust support vector
regression approach to estimate the errors efficiently. Comparative analysis in
the 13-bus and 123-bus IEEE unbalanced feeders shows that the proposed
algorithm achieves superior computational efficiency, with guaranteed accuracy
and robustness against outliers.
