Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

TL;DR

This paper introduces a hybrid data-driven method combining Taylor series expansion and support vector regression to improve the accuracy and robustness of power flow calculations in unbalanced distribution systems, especially under bad data conditions.

Contribution

It develops a novel hybrid algorithm that integrates Taylor expansion with robust regression to enhance power flow analysis in unbalanced systems with data errors.

Findings

Achieves higher computational efficiency compared to traditional methods.

Guarantees accuracy and robustness against measurement outliers.

Effective in unbalanced IEEE test feeders with DER integration.

Abstract

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.