Variational Bayesian Learning With Reliable Likelihood Approximation for Accurate Process Quality Evaluation

Abstract

The State Estimation (SE) method is troubled by heavy computational tasks and poor estimation tracking capability for the large-scale active distribution network. Given the aforementioned difficulty, this paper proposed a novel multi-area Forecasting Aided State Estimation (FASE) strategy to perceive the state of the system effectively. The proposed strategy begins with the implementation of an improved multi-area FASE model. The processing of multi-source measurement data, such as Micro Phasor Measurement Units (μPMUs) and Supervisory Control and Data Acquisition (SCADA), and equivalent load based information interaction reliably complete the FASE of multi areas. Especially, a 3rd degree dimensionality reduction 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.