Work

Robust Forecasting-Aided State Estimation of Active Distribution Network With Multiple Distributed Generators

Year: 2025

Type: article

Abstract: This paper develops a robust variational Bayesian unscented Kalman filter (RVBUKF) to track the state of active distribution networks with distributed energy resources. First, a spherical simplex-based unscented transform (UT) strategy is used to select Sigma points to reduce the computational burden. Subsequently, based on unscented Kalman filter and variational Bayesian theory, a variational update form of the covariance matrix is derived by using the inverse Wishart probability density function. To mitigate the impact of outliers, the Gaussian process regression model trained by historical data is used to synchronously predict observation values to replace abnormal measurements. Finally, simulation experiments are conducted on the distribution system containing distributed generations (DGs). The numerical results demonstrate that the developed method can accurately track state changes in uncertain scenarios while reducing the computational burden of UT by almost half.

Source: IEEE Transactions on Automation Science and Engineering

DOI: 10.1109/tase.2025.3578493

Authors: Dongchen Hou, Yonghui Sun, Kang Sun, Venkata Dinavahi, Yi Wang

Institutions: North China University of Water Resources and Electric Power, Hohai University, University of Alberta, Zhengzhou University

Language: English

Topic: Power System Optimization and Stability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Sciences and Engineering Research Council of Canada, National Natural Science Foundation of China - State Grid Corporation Joint Fund for Smart Grid