Distributed state estimation method based on WLS-AKF hybrid algorithm for active distribution networks

Abstract

Given the difficulty of traditional state estimation methods in meeting the computational efficiency and accuracy requirements of active distribution networks, we propose a distributed state estimation (DSE) method for active distribution networks based on the weighted least squares-adaptive Kalman filter (WLS-AKF) hybrid algorithm. A multi-criteria partition model of an active distribution network that is suitable for DSE has been established. The model comprehensively considers the impact of partitioning results on DSE calculation accuracy and efficiency from the three perspectives of structure, measurement, and performance and solves the model using an improved genetic algorithm. A partition decoupling method is proposed herein, which completely decouples the sub-regions without requiring the measurement configuration of the sub-region boundary nodes and effectively reduces the DSE calculation scale. Furthermore, the paper proposes a DSE algorithm based on WLS-AKF that uses AKF to provide accurate pseudo-measurement of boundary nodes for WLS, which improves the calculation efficiency while ensuring the DSE calculation accuracy. The proposed method is analyzed and verified using the improved IEEE118 node system. The results show that the proposed method has high computational accuracy and efficiency, and can obtain high-precision estimation results in the case of missing data.