A Multiarea Forecasting-Aided State Estimation Strategy for Unbalance Distribution Networks

The state estimation method is troubled by heavy computational tasks and poor estimation tracking capability for the large-scale active distribution network. Given the aforementioned difficulty, in this article, we proposed a novel multiarea forecasting-aided state estimation (FASE) strategy to perceive the state of the system effectively. The proposed strategy begins with the implementation of an improved multiarea FASE model. The processing of multisource measurement data, such as microphasor measurement units and supervisory control and data acquisition, and equivalent load-based information interaction reliably complete the FASE of multiareas. Especially, a third degree dimensionality reduction square root cubature Kalman filter (SR-CKF) algorithm is designed for local FASE model considering the influence of large-scale distribution networks data on the numerical stability of the estimator. The case study shows the advantages of the proposed strategy in estimation accuracy, efficiency, and numerical stability compared with the existing ones.